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RAY KURZWEIL

AUTHOR OF THE NEW YORK TIMES BESTSELLING

THE SINGULARITY IS NEAR

More Praise for How to Create a Mind

“This book is a Rosetta stone for the mystery of human thought. Even more
remarkably, it is a blueprint for creating artificial consciousness that is as
persuasive and emotional as our own. Kurzweil deals with the subject of
consciousness better than anyone from Blackmore to Dennett. His
persuasive thought experiment is of Einstein quality: It forces recognition
of the truth.”

—Martine Rothblatt, chairman and CEO, United Therapeutics; creator
of Sirius XM Satellite Radio

“Kurzweil’s book is a shining example of his prodigious ability to
synthesize ideas from disparate domains and explain them to readers in
simple, elegant language. Just as Chanute’s Progress in Flying Machines
ushered in the era of aviation over a century ago, this book is the harbinger
of the coming revolution in artificial intelligence that will fulfill Kurzweil’s
own prophecies about it.”

—Dileep George, AI scientist; pioneer of hierarchical models of the
neocortex; cofounder of Numenta and Vicarious Systems

“Ray Kurzweil’s understanding of the brain and artificial intelligence will
dramatically impact every aspect of our lives, every industry on Earth, and
how we think about our future. If you care about any of these, read this
book!”

—Peter H. Diamandis, chairman and CEO, X PRIZE; executive
chairman, Singularity University; author of the New York Times
bestseller Abundance: The Future Is Better Than You Think

HOW TO CREATE A MIND

ALSO BY RAY KURZWEIL

Transcend: Nine Steps to Living Well Forever
(with Terry Grossman)

The Singularity Is Near: When Humans Transcend Biology

Fantastic Voyage: Live Long Enough to Live Forever
(with Terry Grossman)

The Age of Spiritual Machines:

When Computers Exceed Human Intelligence

The 10% Solution for a Healthy Life

The Age of Intelligent Machines

HOW TO

CREATE

MIND

THE SECRET OF HUMAN
THOUGHT REVEALED

RAY KURZWEIL

VIKING

VIKING

Published by the Penguin Group

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Lirst published in 2012 by Viking Penguin,

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13579 10 8642

Copyright © Ray Kurzweil, 2012

All rights reserved

“Red” by Amoo Oluseun. Used by permission of the author.

“The picture’s pretty bleak, gentlemen...” from The Far Side by Gary Larson (November 7, 1985).

Used by permission of Creators Syndicate.

Illustration credits

Page 10: Created by Wolfgang Beyer (Creative Commons Attribution-Share Alike 3.0 License). 21:
Photo by Timeline (Creative Commons Attribution-Share Alike 3.0 License). 84 (two figures): Prom “The
Geometric Structure of the Brain Liber Pathways,” by Van J. Wedeen, Douglas L. Rosene, Ruopene Wang,
Guangping Dai, Larzad Mortazavi, Patric Hagmann, Jon H. Kaas, and Wen-Yih I. Tseng, Science, March
30, 2012. Reprinted with permission of AAAS (American Association for the Advancement of Science).

85: Photo provided by Yeatesh (Creative Commons Attribution-Share Alike 3.0 License). 134 (two):

Images by Marvin Minsky. Used by permission of Marvin Minsky. Some credits appear adjacent to the
respective images. Other images designed by Ray Kurzweil, illustrated by Laksman Prank.

Library of Congress Cataloging-in-Publication Data
Kurzweil, Ray.

How to create a mind : the secret of human thought revealed / Ray Kurzweil.

p. cm.

Includes bibliographical references and index.

ISBN: 978-1-101-60110-5

1. Brain—Localization of functions. 2. Self-consciousness (Awareness) 3. Artificial intelligence. I. Title.

QP385.K87 2012

612.8’2—dc23 2012027185

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ALWAYS LEARNING PEARSON

To Leo Oscar Kurzweil. You are entering an extraordinary world.

ACKNOWLEDGMENTS

I’d like to express my gratitude to my wife, Sonya, for her loving patience
through the vicissitudes of the creative process;

To my children, Ethan and Amy; my daughter-in-law, Rebecca; my sister,
Enid; and my new grandson, Leo, for their love and inspiration;

To my mother, Hannah, for supporting my early ideas and inventions,
which gave me the freedom to experiment at a young age, and for keeping my
father alive during his long illness;

To my longtime editor at Viking, Rick Kot, for his leadership, steady and
insightful guidance, and expert editing;

To Loretta Barrett, my literary agent for twenty years, for her astute and
enthusiastic guidance;

To Aaron Kleiner, my long-term business partner, for his devoted
collaboration for the past forty years;

To Amara Angelica for her devoted and exceptional research support;

To Sarah Black for her outstanding research insights and ideas;

To Laksman Frank for his excellent illustrations;

To Sarah Reed for her enthusiastic organizational support;

To Nanda Barker-Hook for her expert organization of my public events on
this and other topics;

To Amy Kurzweil for her guidance on the craft of writing;

To Cindy Mason for her research support and ideas on AI and the mind-
body connection;

To Dileep George for his discerning ideas and insightful discussions by e-
mail and otherwise;

To Martine Rothblatt for her dedication to all of the technologies I discuss
in the book and for our collaborations in developing technologies in these areas;

To the KurzweilAI.net team, who provided significant research and
logistical support for this project, including Aaron Kleiner, Amara Angelica,
Bob Beal, Casey Beal, Celia Black-Brooks, Cindy Mason, Denise Scutellaro,
Joan Walsh, Giulio Prisco, Ken Linde, Laksman Frank, Maria Ellis, Nanda

Barker-Hook, Sandi Dube, Sarah Black, Sarah Brangan, and Sarah Reed;

To the dedicated team at Viking Penguin for all of their thoughtful
expertise, including Clare Ferraro (president), Carolyn Coleburn (director of
publicity), Yen Cheong and Langan Kingsley (publicists), Nancy Sheppard
(director of marketing), Bruce Giffords (production editor), Kyle Davis (editorial
assistant), Fabiana Van Arsdell (production director), Roland Ottewell (copy
editor), Daniel Lagin (designer), and Julia Thomas (jacket designer);

To my colleagues at Singularity University for their ideas, enthusiasm, and
entrepreneurial energy;

To my colleagues who have provided inspired ideas reflected in this
volume, including Barry Ptolemy, Ben Goertzel, David Dalrymple, Dileep
George, Felicia Ptolemy, Francis Ganong, George Gilder, Larry Janowitch,
Laura Deming, Lloyd Watts, Martine Rothblatt, Marvin Minsky, Mickey Singer,
Peter Diamandis, Raj Reddy, Terry Grossman, Tomaso Poggio, and Vlad
Sejnoha;

To my peer expert readers, including Ben Goertzel, David Gamez, Dean
Kamen, Dileep George, Douglas Katz, Harry George, Lloyd Watts, Martine
Rothblatt, Marvin Minsky, Paul Linsay, Rafael Reif, Raj Reddy, Randal Koene,
Dr. Stephen Wolfram, and Tomaso Poggio;

To my in-house and lay readers whose names appear above;

And, finally, to all of the creative thinkers in the world who inspire me
every day.

CONTENTS

INTRODUCTION

1. THOUGHT EXPERIMENTS ON THE WORLD

2. THOUGHT EXPERIMENTS ON THINKING

3. A MODEL OF THE NEOCORTEX: THE PATTERN RECOGNITION

THEORY OF MIND

4. THE BIOLOGICAL NEOCORTEX

5. THE OLD BRAIN

6. TRANSCENDENT ABILITIES

7. THE BIOLOGICALLY INSPIRED DIGITAL NEOCORTEX

8. THE MIND AS COMPUTER

9. THOUGHT EXPERIMENTS ON THE MIND

10. THE LAW OF ACCELERATING RETURNS APPLIED TO THE

BRAIN

11. OBJECTIONS

EPILOGUE

NOTES

INDEX

INTRODUCTION

The Brain—is wider than the Sky —
For—put them side by side —

The one the other will contain
With ease—and You — beside —

The Brain is deeper than the sea —
For—hold them—Blue to Blue —

The one the other will absorb —

As Sponges — Buckets — do —

The Brain is just the weight of God —
For—Heft them—Pound for Pound —
And they will differ—if they do —

As Syllable from Sound
—Emily Dickinson

As the most important phenomenon in the universe, intelligence is capable of
transcending natural limitations, and of transforming the world in its own image.
In human hands, our intelligence has enabled us to overcome the restrictions of
our biological heritage and to change ourselves in the process. We are the only
species that does this.

The story of human intelligence starts with a universe that is capable of
encoding information. This was the enabling factor that allowed evolution to
take place. How the universe got to be this way is itself an interesting story. The
standard model of physics has dozens of constants that need to be precisely what
they are, or atoms would not have been possible, and there would have been no
stars, no planets, no brains, and no books on brains. That the laws of physics are
so precisely tuned to have allowed the evolution of information appears to be
incredibly unlikely. Yet by the anthropic principle, we would not be talking

about it if it were not the case. Where some people see a divine hand, others see
a multiverse spawning an evolution of universes with the boring (non-
information-bearing) ones dying out. But regardless of how our universe got to
be the way it is, we can start our story with a world based on information.

The story of evolution unfolds with increasing levels of abstraction. Atoms
—especially carbon atoms, which can create rich information structures by
linking in four different directions—formed increasingly complex molecules. As
a result, physics gave rise to chemistry.

A billion years later, a complex molecule called DNA evolved, which could
precisely encode lengthy strings of information and generate organisms
described by these “programs.” As a result, chemistry gave rise to biology.

At an increasingly rapid rate, organisms evolved communication and
decision networks called nervous systems, which could coordinate the
increasingly complex parts of their bodies as well as the behaviors that
facilitated their survival. The neurons making up nervous systems aggregated
into brains capable of increasingly intelligent behaviors. In this way, biology
gave rise to neurology, as brains were now the cutting edge of storing and
manipulating information. Thus we went from atoms to molecules to DNA to
brains. The next step was uniquely human.

The mammalian brain has a distinct aptitude not found in any other class of
animal. We are capable of hierarchical thinking, of understanding a structure
composed of diverse elements arranged in a pattern, representing that
arrangement with a symbol, and then using that symbol as an element in a yet
more elaborate configuration. This capability takes place in a brain structure
called the neocortex, which in humans has achieved a threshold of sophistication
and capacity such that we are able to call these patterns ideas. Through an
unending recursive process we are capable of building ideas that are ever more
complex. We call this vast array of recursively linked ideas knowledge. Only
Homo sapiens have a knowledge base that itself evolves, grows exponentially,
and is passed down from one generation to another.

Our brains gave rise to yet another level of abstraction, in that we have used
the intelligence of our brains plus one other enabling factor, an opposable
appendage—the thumb—to manipulate the environment to build tools. These
tools represented a new form of evolution, as neurology gave rise to technology.
It is only because of our tools that our knowledge base has been able to grow
without limit.

Our first invention was the story: spoken language that enabled us to
represent ideas with distinct utterances. With the subsequent invention of written
language we developed distinct shapes to symbolize our ideas. Libraries of

written language vastly extended the ability of our unaided brains to retain and
extend our knowledge base of recursively structured ideas.

There is some debate as to whether other species, such as chimpanzees,
have the ability to express hierarchical ideas in language. Chimps are capable of
learning a limited set of sign language symbols, which they can use to
communicate with human trainers. It is clear, however, that there are distinct
limits to the complexity of the knowledge structures with which chimps are
capable of dealing. The sentences that they can express are limited to specific
simple noun-verb sequences and are not capable of the indefinite expansion of
complexity characteristic of humans. For an entertaining example of the
complexity of human-generated language, just read one of the spectacular
multipage-length sentences in a Gabriel Garda Marquez story or novel—his six-
page story “The Last Voyage of the Ghost” is a single sentence and works quite
well in both Spanish and the English translation.-

The primary idea in my three previous books on technology ( The Age of
Intelligent Machines, written in the 1980s and published in 1989; The Age of
Spiritual Machines, written in the mid- to late 1990s and published in 1999; and
The Singularity Is Near, written in the early 2000s and published in 2005) is that
an evolutionary process inherently accelerates (as a result of its increasing levels
of abstraction) and that its products grow exponentially in complexity and
capability. I call this phenomenon the law of accelerating returns (LOAR), and it
pertains to both biological and technological evolution. The most dramatic
example of the LOAR is the remarkably predictable exponential growth in the
capacity and price/performance of information technologies. The evolutionary
process of technology led invariably to the computer, which has in turn enabled a
vast expansion of our knowledge base, permitting extensive links from one area
of knowledge to another. The Web is itself a powerful and apt example of the
ability of a hierarchical system to encompass a vast array of knowledge while
preserving its inherent structure. The world itself is inherently hierarchical—
trees contain branches; branches contain leaves; leaves contain veins. Buildings
contain floors; floors contain rooms; rooms contain doorways, windows, walls,
and floors.

We have also developed tools that are now enabling us to understand our
own biology in precise information terms. We are rapidly reverse-engineering
the information processes that underlie biology, including that of our brains. We
now possess the object code of life in the form of the human genome, an
achievement that was itself an outstanding example of exponential growth, in
that the amount of genetic data the world has sequenced has approximately

doubled every year for the past twenty years.- We now have the ability to
simulate on computers how sequences of base pairs give rise to sequences of
amino acids that fold up into three-dimensional proteins, from which all of
biology is constructed. The complexity of proteins for which we can simulate
protein folding has been steadily increasing as computational resources continue
to grow exponentially.- We can also simulate how proteins interact with one
another in an intricate three-dimensional dance of atomic forces. Our growing
understanding of biology is one important facet of discovering the intelligent
secrets that evolution has bestowed on us and then using these biologically
inspired paradigms to create ever more intelligent technology.

There is now a grand project under way involving many thousands of
scientists and engineers working to understand the best example we have of an
intelligent process: the human brain. It is arguably the most important effort in
the history of the human-machine civilization. In The Singularity Is Near I made
the case that one corollary of the law of accelerating returns is that other
intelligent species are likely not to exist. To summarize the argument, if they
existed we would have noticed them, given the relatively brief time that elapses
between a civilization’s possessing crude technology (consider that in 1850 the
fastest way to send nationwide information was the Pony Express) to its
possessing technology that can transcend its own planet.- From this perspective,
reverse-engineering the human brain may be regarded as the most important
project in the universe.

The goal of the project is to understand precisely how the human brain
works, and then to use these revealed methods to better understand ourselves, to
fix the brain when needed, and—most relevant to the subject of this book—to
create even more intelligent machines. Keep in mind that greatly amplifying a
natural phenomenon is precisely what engineering is capable of doing. As an
example, consider the rather subtle phenomenon of Bernoulli’s principle, which
states that there is slightly less air pressure over a moving curved surface than
over a moving flat one. The mathematics of how Bernoulli’s principle produces
wing lift is still not yet fully settled among scientists, yet engineering has taken
this delicate insight, focused its powers, and created the entire world of aviation.

In this book I present a thesis I call the pattern recognition theory of mind
(PRTM), which, I argue, describes the basic algorithm of the neocortex (the
region of the brain responsible for perception, memory, and critical thinking). In
the chapters ahead I describe how recent neuroscience research, as well as our
own thought experiments, leads to the inescapable conclusion that this method is
used consistently across the neocortex. The implication of the PRTM combined

with the LOAR is that we will be able to engineer these principles to vastly
extend the powers of our own intelligence.

Indeed this process is already well under way. There are hundreds of tasks
and activities formerly the sole province of human intelligence that can now be
conducted by computers, usually with greater precision and at a vastly greater
scale. Every time you send an e-mail or connect a cell phone call, intelligent
algorithms optimally route the information. Obtain an electrocardiogram, and it
comes back with a computer diagnosis that rivals that of doctors. The same is
true for blood cell images. Intelligent algorithms automatically detect credit card
fraud, fly and land airplanes, guide intelligent weapons systems, help design
products with intelligent computer-aided design, keep track of just-in-time
inventory levels, assemble products in robotic factories, and play games such as
chess and even the subtle game of Go at master levels.

Millions of people witnessed the IBM computer named Watson play the
natural-language game of Jeopardy! and obtain a higher score than the best two
human players in the world combined. It should be noted that not only did
Watson read and “understand” the subtle language in the Jeopardy! query (which
includes such phenomena as puns and metaphors), but it obtained the knowledge
it needed to come up with a response from understanding hundreds of millions
of pages of natural-language documents including Wikipedia and other
encyclopedias on its own. It needed to master virtually every area of human
intellectual endeavor, including history, science, literature, the arts, culture, and
more. IBM is now working with Nuance Speech Technologies (formerly
Kurzweil Computer Products, my first company) on a new version of Watson
that will read medical literature (essentially all medical journals and leading
medical blogs) to become a master diagnostician and medical consultant, using
Nuance’s clinical language-understanding technologies. Some observers have
argued that Watson does not really “understand” the Jeopardy! queries or the
encyclopedias it has read because it is just engaging in “statistical analysis.” A
key point I will describe here is that the mathematical techniques that have
evolved in the field of artificial intelligence (such as those used in Watson and
Siri, the iPhone assistant) are mathematically very similar to the methods that
biology evolved in the form of the neocortex. If understanding language and
other phenomena through statistical analysis does not count as true
understanding, then humans have no understanding either.

Watson’s ability to intelligently master the knowledge in natural-language
documents is coming to a search engine near you, and soon. People are already
talking to their phones in natural language (via Siri, for example, which was also
contributed to by Nuance). These natural-language assistants will rapidly

become more intelligent as they utilize more of the Watson-like methods and as
Watson itself continues to improve.

The Google self-driving cars have logged 200,000 miles in the busy cities
and towns of California (a figure that will undoubtedly be much higher by the
time this book hits the real and virtual shelves). There are many other examples
of artificial intelligence in today’s world, and a great deal more on the horizon.

As further examples of the LOAR, the spatial resolution of brain scanning
and the amount of data we are gathering on the brain are doubling every year.
We are also demonstrating that we can turn this data into working models and
simulations of brain regions. We have succeeded in reverse-engineering key
functions of the auditory cortex, where we process information about sound; the
visual cortex, where we process information from our sight; and the cerebellum,
where we do a portion of our skill formation (such as catching a fly ball).

The cutting edge of the project to understand, model, and simulate the
human brain is to reverse-engineer the cerebral neocortex, where we do our
recursive hierarchical thinking. The cerebral cortex, which accounts for 80
percent of the human brain, is composed of a highly repetitive structure,
allowing humans to create arbitrarily complex structures of ideas.

In the pattern recognition theory of mind, I describe a model of how the
human brain achieves this critical capability using a very clever structure
designed by biological evolution. There are details in this cortical mechanism
that we do not yet fully understand, but we know enough about the functions it
needs to perform that we can nonetheless design algorithms that accomplish the
same purpose. By beginning to understand the neocortex, we are now in a
position to greatly amplify its powers, just as the world of aviation has vastly
amplified the powers of Bernoulli’s principle. The operating principle of the
neocortex is arguably the most important idea in the world, as it is capable of
representing all knowledge and skills as well as creating new knowledge. It is
the neocortex, after all, that has been responsible for every novel, every song,
every painting, every scientific discovery, and the multifarious other products of
human thought.

There is a great need in the field of neuroscience for a theory that ties
together the extremely disparate and extensive observations that are being
reported on a daily basis. A unified theory is a crucial requirement in every
major area of science. In chapter 1 I’ll describe how two daydreamers unified
biology and physics, fields that had previously seemed hopelessly disordered
and varied, and then address how such a theory can be applied to the landscape
of the brain.

Today we often encounter great celebrations of the complexity of the

human brain. Google returns some 30 million links for a search request for
quotations on that topic. (It is impossible to translate this into the number of
actual quotations it is returning, however, as some of the Web sites linked have
multiple quotes, and some have none.) James D. Watson himself wrote in 1992
that “the brain is the last and grandest biological frontier, the most complex thing
we have yet discovered in our universe.” He goes on to explain why he believes
that “it contains hundreds of billions of cells interlinked through trillions of
connections. The brain boggles the mind.”-

I agree with Watson’s sentiment about the brain’s being the grandest
biological frontier, but the fact that it contains many billions of cells and trillions
of connections does not necessarily make its primary method complex if we can
identify readily understandable (and recreatable) patterns in those cells and
connections, especially massively redundant ones.

Let’s think about what it means to be complex. We might ask, is a forest
complex? The answer depends on the perspective you choose to take. You could
note that there are many thousands of trees in the forest and that each one is
different. You could then go on to note that each tree has thousands of branches
and that each branch is completely different. Then you could proceed to describe
the convoluted vagaries of a single branch. Your conclusion might be that the
forest has a complexity beyond our wildest imagination.

But such an approach would literally be a failure to see the forest for the
trees. Certainly there is a great deal of fractal variation among trees and
branches, but to correctly understand the principles of a forest you would do
better to start by identifying the distinct patterns of redundancy with stochastic
(that is, random) variation that are found there. It would be fair to say that the
concept of a forest is simpler than the concept of a tree.

Thus it is with the brain, which has a similar enormous redundancy,
especially in the neocortex. As I will describe in this book, it would be fair to say
that there is more complexity in a single neuron than in the overall structure of
the neocortex.

My goal in this book is definitely not to add another quotation to the
millions that already exist attesting to how complex the brain is, but rather to
impress you with the power of its simplicity. I will do so by describing how a
basic ingenious mechanism for recognizing, remembering, and predicting a
pattern, repeated in the neocortex hundreds of millions of times, accounts for the
great diversity of our thinking. Just as an astonishing diversity of organisms
arises from the different combinations of the values of the genetic code found in
nuclear and mitochondrial DNA, so too does an astounding array of ideas,
thoughts, and skills form based on the values of the patterns (of connections and

synaptic strengths) found in and between our neocortical pattern recognizers. As
MIT neuroscientist Sebastian Seung says, “Identity lies not in our genes, but in
the connections between our brain cells.

We need to distinguish between true complexity of design and apparent
complexity. Consider the famous Mandelbrot set, the image of which has long
been a symbol of complexity. To appreciate its apparent complication, it is useful
to zoom in on its image (which you can access via the links in this endnote).-
There is endless intricacy within intricacy, and they are always different. Yet the
design—the formula—for the Mandelbrot set couldn’t be simpler. It is six
characters long: Z = Z 2 + C, in which Z is a “complex” number (meaning a pair
of numbers) and C is a constant. It is not necessary to fully understand the
Mandelbrot function to see that it is simple. This formula is applied iteratively
and at every level of a hierarchy. The same is true of the brain. Its repeating
structure is not as simple as that of the six-character formula of the Mandelbrot
set, but it is not nearly as complex as the millions of quotations on the brain’s
complexity would suggest. This neocortical design is repeated over and over at
every level of the conceptual hierarchy represented by the neocortex. Einstein
articulated my goals in this book well when he said that “any intelligent fool can
make things bigger and more complex...but it takes...a lot of courage to move
in the opposite direction.”

One view of the display of the Mandelbrot set, a simple formula that is
iteratively applied. As one zooms in on the display, the images constantly
change in apparently complex ways.

So far I have been talking about the brain. But what about the mind? For
example, how does a problem-solving neocortex attain consciousness? And
while we’re on the subject, just how many conscious minds do we have in our
brain? There is evidence that suggests there may be more than one.

Another pertinent question about the mind is, what is free will, and do we
have it? There are experiments that appear to show that we start implementing
our decisions before we are even aware that we have made them. Does that
imply that free will is an illusion?

Finally, what attributes of our brain are responsible for forming our
identity? Am I the same person I was six months ago? Clearly I am not exactly
the same as I was then, but do I have the same identity?

We’ll review what the pattern recognition theory of mind implies about
these age-old questions.

CHAPTER 1

THOUGHT EXPERIMENTS
ON THE WORLD

Darwin’s theory of natural selection came very late in the history of
thought.

Was it delayed because it opposed revealed truth, because it was an
entirely new subject in the history of science, because it was characteristic
only of living things, or because it dealt with purpose and final causes
without postulating an act of creation? I think not. Darwin simply
discovered the role of selection, a kind of causality very different from the
push-pull mechanisms of science up to that time. The origin of a fantastic
variety of living things could be explained by the contribution of which
novel features, possibly of random provenance, made it to survival. There
was little or nothing in physical or biological science that foreshadowed
selection as a causal principle.

—B. F. Skinner

Nothing is at last sacred but the integrity of your own mind.

—Ralph Waldo Emerson

A Metaphor from Geology

In the early nineteenth century geologists pondered a fundamental question.
Great caverns and canyons such as the Grand Canyon in the United States and
Vikos Gorge in Greece (reportedly the deepest canyon in the world) existed all
across the globe. How did these majestic formations get there?

Invariably there was a stream of water that appeared to take advantage of
the opportunity to course through these natural structures, but prior to the mid¬
nineteenth century, it had seemed absurd that these gentle flows could be the
creator of such huge valleys and cliffs. British geologist Charles Lyell (1797-
1875), however, proposed that it was indeed the movement of water that had
carved out these major geological modifications over great periods of time,
essentially one grain of rock at a time. This proposal was initially met with
ridicule, but within two decades Lyell’s thesis achieved mainstream acceptance.

One person who was carefully watching the response of the scientific
community to Lyell’s radical thesis was English naturalist Charles Darwin
(1809-1882). Consider the situation in biology around 1850. The field was
endlessly complex, faced with countless species of animals and plants, any one
of which presented great intricacy. If anything, most scientists resisted any
attempt to provide a unifying theory of nature’s dazzling variation. This diversity
served as a testament to the glory of God’s creation, not to mention to the
intelligence of the scientists who were capable of mastering it.

Darwin approached the problem of devising a general theory of species by
making an analogy with Lyell’s thesis to account for the gradual changes in the
features of species over many generations. He combined this insight with his
own thought experiments and observations in his famous Voyage of the Beagle.
Darwin argued that in each generation the individuals that could best survive in
their ecological niche would be the individuals to create the next generation.

On November 22, 1859, Darwin’s book On the Origin of Species went on
sale, and in it he made clear his debt to Lyell:

I am well aware that this doctrine of natural selection, exemplified in
the above imaginary instances, is open to the same objections which were at
first urged against Sir Charles Lyell’s noble views on “the modern changes
of the earth, as illustrative of geology”; but we now very seldom hear the

action, for instance, of the coast-waves called a trifling and insignificant
cause, when applied to the excavation of gigantic valleys or to the
formation of the longest lines of inland cliffs. Natural selection can act only
by the preservation and accumulation of infinitesimally small inherited
modifications, each profitable to the preserved being; and as modern
geology has almost banished such views as the excavation of a great valley
by a single diluvial wave, so will natural selection, if it be a true principle,
banish the belief of the continued creation of new organic beings, or of any
great and sudden modification in their structure.-

Charles Darwin, author of On the Origin of Species, which
established the idea of biological evolution.

There are always multiple reasons why big new ideas are resisted, and it is
not hard to identify them in Darwin’s case. That we were descended not from
God but from monkeys, and before that, worms, did not sit well with many
commentators. The implication that our pet dog was our cousin, as was the
caterpillar, not to mention the plant it walked on (a millionth or billionth cousin,
perhaps, but still related), seemed a blasphemy to many.

But the idea quickly caught on because it brought coherence to what had
previously been a plethora of apparently unrelated observations. By 1872, with
the publication of the sixth edition of On the Origin of Species, Darwin added
this passage: “As a record of a former state of things, I have retained in the
foregoing paragraphs...several sentences which imply that naturalists believe in
the separate creation of each species; and I have been much censured for having

thus expressed myself. But undoubtedly this was the general belief when the first
edition of the present work appeared.... Now things are wholly changed, and
almost every naturalist admits the great principle of evolution.”-

Over the next century Darwin’s unifying idea deepened. In 1869, only a
decade after the original publication of On the Origin of Species, Swiss
physician Friedrich Miescher (1844-1895) discovered a substance he called
“nuclein” in the cell nucleus, which turned out to be DNA." In 1927 Russian
biologist Nikolai Koltsov (1872-1940) described what he called a “giant
hereditary molecule,” which he said was composed of “two mirror strands that
would replicate in a semi-conservative fashion using each strand as a template.”
His finding was also condemned by many. The communists considered it to be
fascist propaganda, and his sudden, unexpected death has been attributed to the
secret police of the Soviet Union.- In 1953, nearly a century after the publication
of Darwin’s seminal book, American biologist James D. Watson (born in 1928)
and English biologist Francis Crick (1916-2004) provided the first accurate
characterization of the structure of DNA, describing it as a double helix of two
long twisting molecules.- It is worth pointing out that their finding was based on
what is now known as “photo 51,” taken by their colleague Rosalind Franklin
using X-ray crystallography, which was the first representation that showed the
double helix. Given the insights derived from Franklin’s image, there have been
suggestions that she should have shared in Watson and Crick’s Nobel Prize.-

Rosalind Franklin took the critical picture of DNA (using X-ray
crystallography) that enabled Watson and Crick to accurately describe the
structure of DNA for the first time.

With the description of a molecule that could code the program of biology,
a unifying theory of biology was now firmly in place. It provided a simple and
elegant foundation to all of life. Depending only on the values of the base pairs
that make up the DNA strands in the nucleus (and to a lesser degree the
mitochondria), an organism would mature into a blade of grass or a human
being. This insight did not eliminate the delightful diversity of nature, but we
now understand that the extraordinary diversity of nature stems from the great
assortment of structures that can be coded on this universal molecule.

Riding on a Light Beam

At the beginning of the twentieth century the world of physics was upended
through another series of thought experiments. In 1879 a boy was born to a
German engineer and a housewife. He didn’t start to talk until the age of three
and was reported to have had problems in school at the age of nine. At sixteen he
was daydreaming about riding on a moonbeam.

This young boy was aware of English mathematician Thomas Young’s
(1773-1829) experiment in 1803 that established that light is composed of
waves. The conclusion at that time was that light waves must be traveling
through some sort of medium; after all, ocean waves traveled through water and
sound waves traveled through air and other materials. Scientists called the
medium through which light waves travel the “ether.” The boy was also aware of
the 1887 experiment by American scientists Albert Michelson (1852-1931) and
Edward Morley (1838-1923) that attempted to confirm the existence of the
ether. That experiment was based on the analogy of traveling in a rowboat up-
and downstream in a river. If you are paddling at a fixed speed, then your speed
as measured from the shore will be faster if you are paddling with the stream as
opposed to going against it. Michelson and Morley assumed that light would
travel through the ether at a constant speed (that is, at the speed of light). They
reasoned that the speed of sunlight when Earth is traveling toward the sun in its
orbit (as measured from our vantage point on Earth) versus its apparent speed
when Earth is traveling away from the sun must be different (by twice the speed
of Earth). Proving that would confirm the existence of the ether. However, what
they discovered was that there was no difference in the speed of the sunlight
passing Earth regardless of where Earth was in its orbit. Their findings disproved
the idea of the “ether,” but what was really going on? This remained a mystery
for almost two decades.

As this German teenager imagined riding alongside a light wave, he
reasoned that he should be seeing the light waves frozen, in the same way that a
train would appear not to be moving if you rode alongside it at the same speed as
the train. Yet he realized that this was impossible, because the speed of light is
supposed to be constant regardless of your own movement. So he imagined
instead riding alongside the light beam but at a somewhat slower speed. What if
he traveled at 90 percent of the speed of light? If light beams are like trains, he
reasoned, then he should see the light beam traveling ahead of him at 10 percent

of the speed of light. Indeed, that would have to be what observers on Earth
would see. But we know that the speed of light is a constant, as the Michelson-
Morley experiment had shown. Thus he would necessarily see the light beam
traveling ahead of him at the full speed of light. This seemed like a contradiction
—how could it be possible?

The answer became evident to the German boy, whose name, incidentally,
was Albert Einstein (1879-1955), by the time he turned twenty-six. Obviously—
to young Master Einstein —time itself must have slowed down for him. He
explains his reasoning in a paper published in 19057 If observers on Earth were
to look at the young man’s watch they would see it ticking ten times slower.
Indeed, when he got back to Earth, his watch would show that only 10 percent as
much time had passed (ignoring, for the moment, acceleration and deceleration).
From his perspective, however, his watch was ticking normally and the light
beam next to him was traveling at the speed of light. The ten-times slowdown in
the speed of time itself (relative to clocks on Earth) fully explains the apparent
discrepancies in perspective. In the extreme, the slowdown in the passage of
time would reach zero once the speed of travel reached the speed of light; hence
it was impossible to ride along with the light beam. Although it was impossible
to travel at the speed of light, it turned out not to be theoretically impossible to
move faster than the light beam. Time would then move backward.

This resolution seemed absurd to many early critics. How could time itself
slow down, based only on someone’s speed of movement? Indeed, for eighteen
years (from the time of the Michelson-Morley experiment), other thinkers had
been unable to see a conclusion that was so obvious to Master Einstein. The
many others who had considered this problem through the latter part of the
nineteenth century had essentially “fallen off the horse” in terms of following
through on the implications of a principle, sticking instead to their preconceived
notions of how reality must work. (I should probably change that metaphor to
“fallen off the light beam.”)

Einstein’s second mind experiment was to consider himself and his brother
flying through space. They are 186,000 miles apart. Einstein wants to move
faster but he also desires to keep the distance between them the same. So he
signals his brother with a flashlight each time he wants to accelerate. Since he
knows that it will take one second for the signal to reach his brother, he waits a
second (after sending the signal) to initiate his own acceleration. Each time the
brother receives the signal he immediately accelerates. In this way the two
brothers accelerate at exactly the same time and therefore remain a constant
distance apart.

But now consider what we would see if we were standing on Earth. If the

brothers were moving away from us (with Albert in the lead), it would appear to
take less than a second for the light to reach the brother, because he is traveling
toward the light. Also we would see Albert’s brother’s clock as slowing down
(as his speed increases as he is closer to us). For both of these reasons we would
see the two brothers getting closer and closer and eventually colliding. Yet from
the perspective of the two brothers, they remain a constant 186,000 miles apart.

How can this be? The answer— obviously —is that distances contract
parallel to the motion (but not perpendicular to it). So the two Einstein brothers
are getting shorter (assuming they are flying headfirst) as they get faster. This
bizarre conclusion probably lost Einstein more early fans than the difference in
the passage of time.

During the same year, Einstein considered the relationship of matter and
energy with yet another mind experiment. Scottish physicist James Clerk
Maxwell had shown in the 1850s that particles of light called photons had no
mass but nonetheless carried momentum. As a child I had a device called a
Crookes radiometer,- which consisted of an airtight glass bulb that contained a
partial vacuum and a set of four vanes that rotated on a spindle. The vanes were
white on one side and black on the other. The white side of each vane reflected
light, and the black side absorbed light. (That’s why it is cooler to wear a white
T-shirt on a hot day than a black one.) When a light was shined on the device,
the vanes rotated, with the dark sides moving away from the light. This is a
direct demonstration that photons carry enough momentum to actually cause the
vanes of the radiometer to move.-

The issue that Einstein struggled with is that momentum is a function of
mass: Momentum is equal to mass times velocity. Thus a locomotive traveling at
30 miles per hour has a lot more momentum than, say, an insect traveling at the
same speed. How, then, could there be positive momentum for a particle with
zero mass?

Einstein’s mind experiment consisted of a box floating in space. A photon is
emitted inside the box from the left toward the right side. The total momentum
of the system needs to be conserved, so the box would have to recoil to the left
when the photon was emitted. After a certain amount of time, the photon collides
with the right side of the box, transferring its momentum back to the box. The
total momentum of the system is again conserved, so the box now stops moving.

A Crookes radiometer—the vane with four wings rotates when light
shines on it.

So far so good. But consider the perspective from the vantage point of Mr.
Einstein, who is watching the box from the outside. He does not see any outside
influence on the box: No particles—with or without mass—hit it, and nothing
leaves it. Yet Mr. Einstein, according to the scenario above, sees the box move
temporarily to the left and then stop. According to our analysis, each photon
should permanently move the box to the left. Since there have been no external
effects on the box or from the box, its center of mass must remain in the same
place. Yet the photon inside the box, which moves from left to right, cannot
change the center of mass, because it has no mass.

Or does it? Einstein’s conclusion was that since the photon clearly has
energy, and has momentum, it must also have a mass equivalent. The energy of
the moving photon is entirely equivalent to a moving mass. We can compute
what that equivalence is by recognizing that the center of mass of the system
must remain stationary during the movement of the photon. Working out the
math, Einstein showed that mass and energy are equivalent and are related by a
simple constant. However, there was a catch: The constant might be simple, but
it turned out to be enormous; it was the speed of light squared (about 1.7 x 10 17
meters 2 per second 2 —that is, 17 followed by 16 zeroes). Hence we get Einstein’s
famous E = me 2 .— Thus one ounce (28 grams) of mass is equivalent to 600,000
tons of TNT. Einstein’s letter of August 2, 1939, to President Roosevelt
informing him of the potential for an atomic bomb based on this formula ushered

in the atomic age.—

You might think that this should have been obvious earlier, given that
experimenters had noticed that the mass of radioactive substances decreased as a
result of radiation over time. It was assumed, however, that radioactive
substances contained a special high-energy fuel of some sort that was burning
off. That assumption is not all wrong; it’s just that the fuel that was being
“burned off” was simply mass.

There are several reasons why I have opened this book with Darwin’s and
Einstein’s mind experiments. First of all, they show the extraordinary power of
the human brain. Without any equipment at all other than a pen and paper to
draw the stick figures in these simple mind experiments and to write down the
fairly simple equations that result from them, Einstein was able to overthrow the
understanding of the physical world that dated back two centuries, deeply
influence the course of history (including World War II), and usher in the nuclear
age.

It is true that Einstein relied on a few experimental findings of the
nineteenth century, although these experiments also did not use sophisticated
equipment. It is also true that subsequent experimental validation of Einstein’s
theories has used advanced technologies, and if these had not been developed we
would not have the validation that we possess today that Einstein’s ideas are
authentic and significant. However, such factors do not detract from the fact that
these famous thought experiments reveal the power of human thinking at its
finest.

Einstein is widely regarded as the leading scientist of the twentieth century
(and Darwin would be a good contender for that honor in the nineteenth
century), yet the mathematics underlying his theories is ultimately not very
complicated. The thought experiments themselves were straightforward. We
might wonder, then, in what respect could Einstein be considered particularly
smart. We’ll discuss later exactly what it was that he was doing with his brain
when he came up with his theories, and where that quality resides.

Conversely, this history also demonstrates the limitations of human
thinking. Einstein was able to ride his light beam without falling off (albeit he
concluded that it was impossible to actually ride a light beam), but how many
thousands of other observers and thinkers were completely unable to think
through these remarkably uncomplicated exercises? One common failure is the
difficulty that most people have in discarding and transcending the ideas and
perspectives of their peers. There are other inadequacies as well, which we will
discuss in more detail after we have examined how the neocortex works.

A Unified Model of the Neocortex

The most important reason I am sharing what are perhaps the most famous
thought experiments in history is as an introduction to using the same approach
with respect to the brain. As you will see, we can get remarkably far in figuring
out how human intelligence works through some simple mind experiments of
our own. Considering the subject matter involved, mind experiments should be a
very appropriate approach.

If a young man’s idle thoughts and the use of no equipment other than pen
and paper were sufficient to revolutionize our understanding of physics, then we
should be able to make reasonable progress with a phenomenon with which we
are much more familiar. After all, we experience our thinking every moment of
our waking lives—and our dreaming lives as well.

After we construct a model of how thinking works through this process of
self-reflection, we’ll examine to what extent we can confirm it through the latest
observations of actual brains and the state of the art in re-creating these
processes in machines.

CHAPTER 2

THOUGHT EXPERIMENTS
ON THINKING

I very rarely think in words at all. A thought comes, and I may try to
express it in words afterwards.

—Albert Einstein

The brain is a three-pound mass you can hold in your hand that can
conceive of a universe a hundred billion light years across.

—Marian Diamond

What seems astonishing is that a mere three-pound object, made of the
same atoms that constitute everything else under the sun, is capable of
directing virtually everything that humans have done: flying to the moon
and hitting seventy home runs, writing Hamlet and building the Taj Mahal
—even unlocking the secrets of the brain itself.

—Joel Havemann

I started thinking about thinking around 1960, the same year that I discovered
the computer. You would be hard pressed today to find a twelve-year-old who
does not use a computer, but back then there were only a handful of them in my
hometown of New York City. Of course these early devices did not fit in your
hand, and the first one I got access to took up a large room. In the early 1960s I
did some programming on an IBM 1620 to do analyses of variance (a statistical
test) on data that had been collected by studying a program for early childhood
education, a forerunner to Head Start. Hence there was considerable drama
involved in the effort, as the fate of this national educational initiative rode on
our work. The algorithms and data being analyzed were sufficiently complex

that we were not able to anticipate what answers the computer would come up
with. The answers were, of course, determined by the data, but they were not
predictable. It turns out that the distinction between being determined and being
predictable is an important one, to which I will return.

I remember how exciting it was when the front-panel lights dimmed right
before the algorithm finished its deliberations, as if the computer were deep in
thought. When people came by, eager to get the next set of results, I would point
to the gently flashing lights and say, “It’s thinking.” This both was and wasn’t a
joke—it really did seem to be contemplating the answers—and staff members
started to ascribe a personality to the machine. It was an anthropomorphization,
perhaps, but it did get me to begin to consider in earnest the relationship between
thinking and computing.

In order to assess the extent to which my own brain is similar to the
computer programs I was familiar with, I began to think about what my brain
must be doing as it processed information. I have continued this investigation for
fifty years. What I will describe below about our current understanding of how
the brain works will sound very different from the standard concept of a
computer. Fundamentally, however, the brain does store and process
information, and because of the universality of computation—a concept to which
I will also return—there is more of a parallel between brains and computers than
may be apparent.

Each time I do something—or think of something—whether it is brushing
my teeth, walking across the kitchen, contemplating a business problem,
practicing on a music keyboard, or coming up with a new idea, I reflect on how I
was able to accomplish it. I think even more about all of the things that I am not
able to do, as the limitations of human thought provide an equally important set
of clues. Thinking so much about thinking might very well be slowing me down,
but I have been hopeful that such exercises in self-reflection will enable me to
refine my mental methods.

To raise our own awareness of how our brains work, let’s consider a series
of mind experiments.

Try this: Recite the alphabet.

You probably remember this from childhood and can do it easily.

Okay, now try this: Recite the alphabet backward.

Unless you have studied the alphabet in this order, you are likely to find it
impossible to do. On occasion someone who has spent a significant amount of
time in an elementary school classroom where the alphabet is displayed will be
able to call up his visual memory and then read it backward from that. Even this
is difficult, though, because we do not actually remember whole images.

Reciting the alphabet backward should be a simple task, as it involves exactly
the same information as reciting it forward, yet we are generally unable to do it.

Do you remember your social security number? If you do, can you recite it
backward without first writing it down? How about the nursery rhyme “Mary
Had a Little Lamb”? Computers can do this trivially. Yet we fail at it unless we
specifically learn the backward sequence as a new series. This tells us something
important about how human memory is organized.

Of course, we are able to perform this task easily if we write down the
sequence and then read it backward. In doing so we are using a technology—
written language—to compensate for one of the limitations of our unaided
thinking, albeit a very early tool. (It was our second invention, with spoken
language as the first.) This is why we invent tools—to compensate for our
shortcomings.

This suggests that our memories are sequential and in order. They can
be accessed in the order that they are remembered. We are unable to
directly reverse the sequence of a memory.

We also have some difficulty starting a memory in the middle of a
sequence. If I learn to play a piece of music on the piano, I generally can’t just
begin it at an arbitrary point in its middle. There are a few points at which I can
jump in, because my sequential memory of the piece is organized in segments. If
I try to start in the middle of a segment, though, I need to revert to sight-reading
until my sequential memory kicks in.

Next, try this: Recall a walk that you took in the last day or so. What do you
remember about it?

This mind experiment works best if you took a walk very recently, such as
earlier today or yesterday. (You can also substitute a drive, or basically any
activity during which you moved across some terrain.)

It is likely that you don’t remember much about the experience. Who was
the fifth person you encountered (not just including people you know)? Did you
see an oak tree? A mailbox? What did you see when you turned the first corner?
If you passed some stores, what was in the second window? Perhaps you can
reconstruct the answers to some of these questions from the few clues that you
do remember, but it is likely that you remember relatively few details, even
though this is a very recent experience.

If you take walks regularly, think back to the first walk you took last month
(or to the first trip to the office last month, if you commute). You probably
cannot recall the specific walk or commute at all, and if you do, you doubtless
recall even fewer details about it than about your walk today.

I will later discuss the issue of consciousness and make the point that we

tend to equate consciousness with our memory of events. The primary reason we
believe that we are not conscious when under anesthesia is that we don’t
remember anything from that period (albeit there are intriguing—and disturbing
—exceptions to this). So with regard to the walk I took this morning, was I not
conscious during most of it? It’s a reasonable question, given that I remember
almost nothing about what I saw or even what I was thinking about.

There happen to be a few things I do remember from my walk this morning.
I recall thinking about this book, but I couldn’t tell you exactly what those
thoughts were. I also recall passing a woman pushing a baby carriage. I
remember that the woman was attractive, and that the baby was cute as well. I
recall two thoughts I had in connection with this experience: This baby is
adorable, like my new grandson, and What is this baby perceiving in her visual
surroundings? I cannot recall what either of them was wearing or the color of
their hair. (My wife will tell you that that is typical.) Although I am unable to
describe anything specific about their appearance, I do have some ineffable
sense of what the mom looked like and believe I could pick out her picture from
among those of several different women. So while there must be something
about her appearance that I have retained in my memory, if I think about the
woman, baby carriage, and baby, I am unable to visualize them. There is no
photograph or video of this event in my mind. It is hard to describe exactly what
is in my mind about this experience.

I also recall having passed a different woman with a baby carriage on a
walk a few weeks earlier. In that case I don’t believe I could even recognize that
woman’s picture. That memory is now much dimmer than it must have been
shortly after that walk.

Next, think about people whom you have encountered only once or twice.
Can you visualize them clearly? If you are a visual artist, then you may have
learned this observational skill, but typically we are unable to visualize people
we’ve only casually come across to draw or describe them sufficiently but would
have little difficulty in recognizing a picture of them.

This suggests that there are no images, videos, or sound recordings
stored in the brain. Our memories are stored as sequences of patterns.
Memories that are not accessed dim over time. When police sketch artists
interview a crime victim, they do not ask, “What did the perpetrator’s eyebrows
look like?” Rather, they will show a series of images of eyebrows and ask the
victim to select one. The correct set of eyebrows will trigger the recognition of
the same pattern that is stored in the victim’s memory.

Let’s now consider faces that you know well. Can you recognize any of
these people ?

You are undoubtedly able to recognize these familiar personalities, even
though they are partially covered or distorted. This represents a key strength of
human perception: We can recognize a pattern even if only part of it is
perceived (seen, heard, felt) and even if it contains alterations. Our
recognition ability is apparently able to detect invariant features of a
pattern—characteristics that survive real-world variations. The apparent
distortions in a caricature or in certain forms of art such as impressionism
emphasize the patterns of an image (person, object) that we recognize while
changing other details. The world of art is actually ahead of the world of science
in appreciating the power of the human perceptual system. We use the same
approach when we recognize a melody from only a few notes.

Now consider this image:

The image is ambiguous—the corner indicated by the black region may be
an inside corner or an outside corner. At first you are likely to perceive it one
way or the other, though with some effort you can change your perception to the
alternate interpretation. Once your mind has fixed on an understanding, however,
it may be difficult to see the other perspective. (This turns out to be true of
intellectual perspectives as well.) Your brain’s interpretation of the image

actually influences your experience of it. When the corner appears to be an
inside one, your brain will interpret the grey region as a shadow, so it does not
seem to be as dark as when you interpret the corner as being an outside one.

Thus our conscious experience of our perceptions is actually changed by
our interpretations.

Consider that we see what we expect to _

I’m confident that you were able to complete the above sentence.

Had I written out the last word, you would have needed only to glance at it
momentarily to confirm that it was what you had expected.

This implies that we are constantly predicting the future and
hypothesizing what we will experience. This expectation influences what we
actually perceive. Predicting the future is actually the primary reason that we
have a brain.

Consider an experience that we all have on a regular basis: A memory from
years ago inexplicably pops into your head.

Often this will be a memory of a person or an event that you haven’t
thought about for a long time. It is evident that something has triggered the
memory. The train of thought that did so may be apparent and something you are
able to articulate. At other times you may be aware of the sequence of thoughts
that led to the memory but would have a hard time expressing it. Often the
trigger is quickly lost, so the memory appears to have come from nowhere. I
often experience these random memories while doing routine procedures such as
brushing my teeth. Sometimes I may be aware of the connection—the toothpaste
falling off the toothbrush might remind me of the paint falling off a brush in a
painting class I took in college. Sometimes I have only a vague sense of the
connection, or none at all.

A related phenomenon that everyone experiences frequently is trying to
think of a name or a word. The procedure we use in this circumstance is to try to
remind ourselves of triggers that may unlock the memory. (For example: Who
played Queen Padme in Revenge of the Sith? Let’s see, it’s that same actress
who was the star in a recent dark movie about dancing, that was Black Swan, oh
yes, Natalie Portman .) Sometimes we adopt idiosyncratic mnemonics to help us
remember. (For example: She’s always slim, not portly, oh yes, Portman, Natalie
Portman .) Some of our memories are sufficiently robust that we can go directly
from a question (such as who played Queen Padme) to the answer; often we
need to go through a series of triggers until we find one that works. It’s very
much like having the right Web link. Memories can indeed become lost like a
Web page to which no other page links to (at least no page that we can find).

While executing routine procedures—such as putting on a shirt—watch

yourself performing them, and consider the extent to which you follow the same
sequence of steps each time. From my own observation (and as I mentioned, I
am constantly trying to observe myself), it is likely that you follow very much
the same steps each time you perform a particular routine task, though there may
be additional modules added. For example, most of my shirts do not require cuff
links, but when one does, that involves a further series of tasks.

The lists of steps in my mind are organized in hierarchies. I follow a routine
procedure before going to sleep. The first step is to brush my teeth. But this
action is in turn broken into a smaller series of steps, the first of which is to put
toothpaste on the toothbrush. That step in turn is made up of yet smaller steps,
such as finding the toothpaste, removing the cap, and so on. The step of finding
the toothpaste also has steps, the first of which is to open the bathroom cabinet.
That step in turn requires steps, the first of which is to grab the outside of the
cabinet door. This nesting actually continues down to a very fine grain of
movements, so that there are literally thousands of little actions constituting my
nighttime routine. Although I may have difficulty remembering details of a walk
I took just a few hours ago, I have no difficulty recalling all of these many steps
in preparing for bed—so much so that I am able to think about other things while
I go through these procedures. It is important to point out that this list is not
stored as one long list of thousands of steps—rather, each of our routine
procedures is remembered as an elaborate hierarchy of nested activities.

The same type of hierarchy is involved in our ability to recognize
objects and situations. We recognize the faces of people we know well and also
recognize that these faces contain eyes, a nose, a mouth, and so on—a hierarchy
of patterns that we use in both our perceptions and our actions. The use of
hierarchies allows us to reuse patterns. For example, we do not need to relearn
the concept of a nose and a mouth each time we are introduced to a new face.

In the next chapter , we’ll put the results of these thought experiments
together into a theory of how the neocortex must work. I will argue that they
reveal essential attributes of our thinking that are uniform, from finding the
toothpaste to writing a poem.

CHAPTER 3

A MODEL OF THE
NEOCORTEX: THE PATTERN
RECOGNITION THEORY OF

MIND

The brain is a tissue. It is a complicated, intricately woven tissue, like
nothing else we know of in the universe, but it is composed of cells, as any
tissue is. They are, to be sure, highly specialized cells, but they function
according to the laws that govern any other cells. Their electrical and
chemical signals can be detected, recorded and interpreted and their
chemicals can be identified; the connections that constitute the brain’s
woven feltwork can be mapped. In short, the brain can be studied, just as
the kidney can.

—David H. Hubei, neuroscientist

Suppose that there be a machine, the structure of which produces thinking,
feeling, and perceiving; imagine this machine enlarged but preserving the
same proportions, so you could enter it as if it were a mill. This being
supposed, you might visit inside; but what would you observe there?
Nothing but parts which push and move each other, and never anything that
could explain perception.

—Gottfried Wilhelm Leibniz

A Hierarchy of Patterns

I have repeated the simple experiments and observations described in the
previous chapter thousands of times in myriad contexts. The conclusions from
these observations necessarily constrain my explanation for what the brain must
be doing, just as the simple experiments on time, space, and mass that were
conducted in the early and late nineteenth century necessarily constrained the
young Master Einstein’s reflections on how the universe functioned. In the
discussion that follows I’ll also factor in some very basic observations from
neuroscience, attempting to avoid the many details that are still in contention.

First, let me explain why this section specifically discusses the neocortex
(from the Latin meaning “new rind”). We do know the neocortex is responsible
for our ability to deal with patterns of information and to do so in a hierarchical
fashion. Animals without a neocortex (basically nonmammals) are largely
incapable of understanding hierarchies.- Understanding and leveraging the
innately hierarchical nature of reality is a uniquely mammalian trait and results
from mammals’ unique possession of this evolutionarily recent brain structure.
The neocortex is responsible for sensory perception, recognition of everything
from visual objects to abstract concepts, controlling movement, reasoning from
spatial orientation to rational thought, and language—basically, what we regard
as “thinking.”

The human neocortex, the outermost layer of the brain, is a thin, essentially
two-dimensional structure with a thickness of about 2.5 millimeters (about a
tenth of an inch). In rodents, it is about the size of a postage stamp and is
smooth. An evolutionary innovation in primates is that it became intricately
folded over the top of the rest of the brain with deep ridges, grooves, and
wrinkles to increase its surface area. Due to its elaborate folding, the neocortex
constitutes the bulk of the human brain, accounting for 80 percent of its weight.
Homo sapiens developed a large forehead to allow for an even larger neocortex;
in particular we have a frontal lobe where we deal with the more abstract
patterns associated with high-level concepts.

This thin structure is basically made up of six layers, numbered I (the
outermost layer) to VI. The axons emerging from the neurons in layers II and III
project to other parts of the neocortex. The axons (output connections) from
layers V and VI are connected primarily outside of the neocortex to the

thalamus, brain stem, and spinal cord. The neurons in layer IV receive synaptic
(input) connections from neurons that are outside the neocortex, especially in the
thalamus. The number of layers varies slightly from region to region. Layer IV is
very thin in the motor cortex, because in that area it largely does not receive
input from the thalamus, brain stem, or spinal cord. Conversely, in the occipital
lobe (the part of the neocortex usually responsible for visual processing), there
are three additional sublayers that can be seen in layer IV, due to the
considerable input flowing into this region, including from the thalamus.

A critically important observation about the neocortex is the extraordinary
uniformity of its fundamental structure. This was first noticed by American
neuroscientist Vernon Mountcastle (born in 1918). In 1957 Mountcastle
discovered the columnar organization of the neocortex. In 1978 he made an
observation that is as significant to neuroscience as the Michelson-Morley ether-
disproving experiment of 1887 were to physics. That year he described the
remarkably unvarying organization of the neocortex, hypothesizing that it was
composed of a single mechanism that was repeated over and over again,- and
proposing the cortical column as that basic unit. The differences in the height of
certain layers in different regions noted above are simply differences in the
amount of interconnectivity that the regions are responsible for dealing with.

Mountcastle hypothesized the existence of mini-columns within columns,
but this theory became controversial because there were no visible demarcations
of such smaller structures. However, extensive experimentation has revealed that
there are in fact repeating units within the neuron fabric of each column. It is my
contention that the basic unit is a pattern recognizer and that this constitutes the
fundamental component of the neocortex. In contrast to Mountcastle’s notion of
a mini-column, there is no specific physical boundary to these recognizers, as
they are placed closely one to the next in an interwoven fashion, so the cortical
column is simply an aggregate of a large number of them. These recognizers are
capable of wiring themselves to one another throughout the course of a lifetime,
so the elaborate connectivity (between modules) that we see in the neocortex is
not prespecified by the genetic code, but rather is created to reflect the patterns
we actually learn over time. I will describe this thesis in more detail, but I
maintain that this is how the neocortex must be organized.

It should be noted, before we further consider the structure of the neocortex,
that it is important to model systems at the right level. Although chemistry is
theoretically based on physics and could be derived entirely from physics, this
would be unwieldy and infeasible in practice, so chemistry has established its
own rules and models. Similarly, we should be able to deduce the laws of
thermodynamics from physics, but once we have a sufficient number of particles

to call them a gas rather than simply a bunch of particles, solving equations for
the physics of each particle interaction becomes hopeless, whereas the laws of
thermodynamics work quite well. Biology likewise has its own rules and
models. A single pancreatic islet cell is enormously complicated, especially if we
model it at the level of molecules; modeling what a pancreas actually does in
terms of regulating levels of insulin and digestive enzymes is considerably less
complex.

The same principle applies to the levels of modeling and understanding in
the brain. It is certainly a useful and necessary part of reverse-engineering the
brain to model its interactions at the molecular level, but the goal of the effort
here is essentially to refine our model to account for how the brain processes
information to produce cognitive meaning.

American scientist Herbert A. Simon (1916-2001), who is credited with
cofounding the field of artificial intelligence, wrote eloquently about the issue of
understanding complex systems at the right level of abstraction. In describing an
AI program he had devised called EPAM (elementary perceiver and memorizer),
he wrote in 1973, “Suppose you decided that you wanted to understand the
mysterious EPAM program that I have. I could provide you with two versions of
it. One would be...the form in which it was actually written—with its whole
structure of routines and subroutines.... Alternatively, I could provide you with a
machine-language version of EPAM after the whole translation had been carried
out—after it had been flattened so to speak.... I don’t think I need argue at
length which of these two versions would provide the most parsimonious, the
most meaningful, the most lawful description.... I will not even propose to you
the third...of providing you with neither program, but instead with the
electromagnetic equations and boundary conditions that the computer, viewed as
a physical system, would have to obey while behaving as EPAM. That would be
the acme of reduction and incomprehensibility.

There are about a half million cortical columns in a human neocortex, each
occupying a space about two millimeters high and a half millimeter wide and
containing about 60,000 neurons (resulting in a total of about 30 billion neurons
in the neocortex). A rough estimate is that each pattern recognizer within a
cortical column contains about 100 neurons, so there are on the order of 300
million pattern recognizers in total in the neocortex.

As we consider how these pattern recognizers work, let me begin by saying
that it is difficult to know precisely where to begin. Everything happens
simultaneously in the neocortex, so there is no beginning and no end to its
processes. I will frequently need to refer to phenomena that I have not yet
explained but plan to come back to, so please bear with these forward references.

Human beings have only a weak ability to process logic, but a very deep
core capability of recognizing patterns. To do logical thinking, we need to use
the neocortex, which is basically a large pattern recognizer. It is not an ideal
mechanism for performing logical transformations, but it is the only facility we
have for the job. Compare, for example, how a human plays chess to how a
typical computer chess program works. Deep Blue, the computer that defeated
Garry Kasparov, the human world chess champion, in 1997 was capable of
analyzing the logical implications of 200 million board positions (representing
different move-countermove sequences) every second. (That can now be done,
by the way, on a few personal computers.) Kasparov was asked how many
positions he could analyze each second, and he said it was less than one. How is
it, then, that he was able to hold up to Deep Blue at all? The answer is the very
strong ability humans have to recognize patterns. However, we need to train this
facility, which is why not everyone can play master chess.

Kasparov had learned about 100,000 board positions. That’s a real number
—we have established that a human master in a particular field has mastered
about 100,000 chunks of knowledge. Shakespeare composed his plays with
100,000 word senses (employing about 29,000 distinct words, but using most of
them in multiple ways). Medical expert systems that have been built to represent
the knowledge of a human medical physician have shown that a typical human
medical specialist has mastered about 100,000 concepts in his or her domain.
Recognizing a chunk of knowledge from this store is not straightforward, as a
particular item will present itself a little bit differently each time it is
experienced.

Armed with his knowledge, Kasparov looks at the chessboard and
compares the patterns that he sees to all 100,000 board situations that he has
mastered, and he does all 100,000 comparisons simultaneously. There is
consensus on this point: All of our neurons are processing—considering the
patterns—at the same time. That does not mean that they are all firing
simultaneously (we would probably fall to the floor if that happened), but while
doing their processing are considering the possibility of firing.

How many patterns can the neocortex store? We need to factor in the
phenomenon of redundancy. The face of a loved one, for example, is not stored
once but on the order of thousands of times. Some of these repetitions are largely
the same image of the face, whereas most show different perspectives of it,
different lighting, different expressions, and so on. None of these repeated
patterns are stored as images per se (that is, as two-dimensional arrays of pixels).
Rather, they are stored as lists of features where the constituent elements of a
pattern are themselves patterns. We’ll describe below more precisely what these

hierarchies of features look like and how they are organized.

If we take the core knowledge of an expert as consisting of about 100,000
“chunks” of knowledge (that is, patterns) with a redundancy estimate of about
100 to 1, that gives us a requirement of 10 million patterns. This core expert
knowledge is built on more general and extensive professional knowledge, so we
can increase the order of magnitude of patterns to about 30 to 50 million. Our
everyday “commonsense” knowledge as a human being is even greater; “street
smarts” actually require substantially more of our neocortex than “book smarts.”
Including this brings our estimate to well over 100 million patterns, taking into
account the redundancy factor of about 100. Note that the redundancy factor is
far from fixed—very common patterns will have a redundancy factor well into
the thousands, whereas a brand-new phenomenon may have a redundancy factor
of less than 10.

As I will discuss below, our procedures and actions also comprise patterns
and are likewise stored in regions of the cortex, so my estimate of the total
capacity of the human neocortex is on the order of low hundreds of millions of
patterns. This rough tally correlates well with the number of pattern recognizers
that I estimated above at about 300 million, so it is a reasonable conclusion that
the function of each neocortical pattern recognizer is to process one iteration
(that is, one copy among the multiple redundant copies of most patterns in the
neocortex) of a pattern. Our estimates of the number of patterns that a human
brain is capable of dealing with (including necessary redundancy) and the
number of physical pattern recognizers happen to be the same order of
magnitude. It should be noted here that when I refer to “processing” a pattern, I
am referring to all of the things we are able to do with a pattern: learn it, predict
it (including parts of it), recognize it, and implement it (either by thinking about
it further or through a pattern of physical movement).

Three hundred million pattern processors may sound like a large number,
and indeed it was sufficient to enable Homo sapiens to develop verbal and
written language, all of our tools, and other diverse creations. These inventions
have built upon themselves, giving rise to the exponential growth of the
information content of technologies as described in my law of accelerating
returns. No other species has achieved this. As I discussed, a few other species,
such as chimpanzees, do appear to have a rudimentary ability to understand and
form language and also to use primitive tools. They do, after all, also have a
neocortex, but their abilities are limited due to its smaller size, especially of the
frontal lobe. The size of our own neocortex has exceeded a threshold that has
enabled our species to build ever more powerful tools, including tools that can
now enable us to understand our own intelligence. Ultimately our brains,

combined with the technologies they have fostered, will permit us to create a
synthetic neocortex that will contain well beyond a mere 300 million pattern
processors. Why not a billion? Or a trillion?

The Structure of a Pattern

The pattern recognition theory of mind that I present here is based on the
recognition of patterns by pattern recognition modules in the neocortex. These
patterns (and the modules) are organized in hierarchies. I discuss below the
intellectual roots of this idea, including my own work with hierarchical pattern
recognition in the 1980s and 1990s and Jeff Hawkins (born in 1957) and Dileep
George’s (born in 1977) model of the neocortex in the early 2000s.

Each pattern (which is recognized by one of the estimated 300 million
pattern recognizers in the neocortex) is composed of three parts. Part one is the
input, which consists of the lower-level patterns that compose the main pattern.
The descriptions for each of these lower-level patterns do not need to be repeated
for each higher-level pattern that references them. For example, many of the
patterns for words will include the letter “A.” Each of these patterns does not
need to repeat the description of the letter “A” but will use the same description.
Think of it as being like a Web pointer. There is one Web page (that is, one
pattern) for the letter “A,” and all of the Web pages (patterns) for words that
include “A” will have a link to the “A” page (to the “A” pattern). Instead of Web
links, the neocortex uses actual neural connections. There is an axon from the
“A” pattern recognizer that connects to multiple dendrites, one for each word
that uses “A.” Keep in mind also the redundancy factor: There is more than one
pattern recognizer for the letter “A.” Any of these multiple “A” pattern
recognizers can send a signal up to the pattern recognizers that incorporate “A.”

S' Inhibitory ® Axon ^

signals from (output) Pattern expected

above (signal tram above)

Size parameter (expected
size on some dimension
such as time or distance) for
this lower-level pattern

Weight (importance) of
this lower-level pattern

Expected variability of
the size of this

Each dendrite sending signals into the lower-level pattern
module represents the presence of a
lower-level pattern (it also encodes size
information). A dendrite sending signals out of
the module indicates that the corresponding
lower-level pattern is expected.

The second part of each pattern is the pattern’s name. In the world of
language, this higher-level pattern is simply the word “apple.” Although we
directly use our neocortex to understand and process every level of language,
most of the patterns it contains are not language patterns per se. In the neocortex
the “name” of a pattern is simply the axon that emerges from each pattern
processor; when that axon fires, its corresponding pattern has been recognized.
The firing of the axon is that pattern recognizer shouting the name of the pattern:
“Hey guys, I just saw the written word ‘apple.’”

Three redundant (but somewhat different) patterns for “A” feeding up to
higher-level patterns that incorporate “A.”

The third and final part of each pattern is the set of higher-level patterns
that it in turn is part of. For the letter “A,” this is all of the words that include
“A.” These are, again, like Web links. Each recognized pattern at one level
triggers the next level that part of that higher-level pattern is present. In the
neocortex, these links are represented by physical dendrites that flow into
neurons in each cortical pattern recognizer. Keep in mind that each neuron can
receive inputs from multiple dendrites yet produces a single output on an axon.
That axon, however, can then in turn transmit to multiple dendrites.

To take some simple examples, the simple patterns on the next page are a
small subset of the patterns used to make up printed letters. Note that every level
constitutes a pattern. In this case, the shapes are patterns, the letters are patterns,
and the words are also patterns. Each of these patterns has a set of inputs, a
process of pattern recognition (based on the inputs that take place in the
module), and an output (which feeds to the next higher level of pattern
recognizer).

Southwest to north-central connection:

Southeast to north-central connection:

Horizontal crossbar:

Leftmost vertical line:

Concave region facing south:

Bottom horizontal line:

Top horizontal line:

Middle horizontal line:

Loop constituting upper region:

The above patterns are constituents of the next higher level of pattern,
which is a category called printed letters (there is no such formal category within
the neocortex, however; indeed, there are no formal categories).

“A”:

Two different patterns, either of which constitutes “A,” and two
different patterns at a higher level (“APPLE” and “PEAR”) of which “A” is
a part.

“D”-

Patterns that are part of the higher-level pattern “L.”

f.

E

t

▲ ▲ ▲ ▲

1

Patterns that are part of the higher-level pattern “E.”

These letter patterns feed up to an even higher-level pattern in a category
called words. (The word “words” is our language category for this concept, but
the neocortex just treats them only as patterns.)

“APPLE”:

APPLE

tt t t

Ai p H

E

In a different part of the cortex is a comparable hierarchy of pattern
recognizers processing actual images of objects (as opposed to printed letters). If
you are looking at an actual apple, low-level recognizers will detect curved
edges and surface color patterns leading up to a pattern recognizer firing its axon
and saying in effect, “Hey guys, I just saw an actual apple.” Yet other pattern
recognizers will detect combinations of frequencies of sound leading up to a
pattern recognizer in the auditory cortex that might fire its axon indicating, “I
just heard the spoken word ‘apple.’”

Keep in mind the redundancy factor—we don’t just have a single pattern
recognizer for “apple” in each of its forms (written, spoken, visual). There are
likely to be hundreds of such recognizers firing, if not more. The redundancy not

only increases the likelihood that you will successfully recognize each instance
of an apple but also deals with the variations in real-world apples. For apple
objects, there will be pattern recognizers that deal with the many varied forms of
apples: different views, colors, shadings, shapes, and varieties.

Also keep in mind that the hierarchy shown above is a hierarchy of
concepts. These recognizers are not physically placed above each other; because
of the thin construction of the neocortex, it is physically only one pattern
recognizer high. The conceptual hierarchy is created by the connections between
the individual pattern recognizers.

An important attribute of the PRTM is how the recognitions are made inside
each pattern recognition module. Stored in the module is a weight for each input
dendrite indicating how important that input is to the recognition. The pattern
recognizer has a threshold for firing (which indicates that this pattern recognizer
has successfully recognized the pattern it is responsible for). Not every input
pattern has to be present for a recognizer to fire. The recognizer may still fire if
an input with a low weight is missing, but it is less likely to fire if a high-
importance input is missing. When it fires, a pattern recognizer is basically
saying, “The pattern I am responsible for is probably present.”

Successful recognition by a module of its pattern goes beyond just counting
the input signals that are activated (even a count weighted by the importance
parameter). The size (of each input) matters. There is another parameter (for
each input) indicating the expected size of the input, and yet another indicating
how variable that size is. To appreciate how this works, suppose we have a
pattern recognizer that is responsible for recognizing the spoken word “steep.”
This spoken word has four sounds: [s], [t], [E], and [p]. The [t] phoneme is what
is known as a “dental consonant,” meaning that it is created by the tongue
creating a burst of noise when air breaks its contact with the upper teeth. It is
essentially impossible to articulate the [t] phoneme slowly. The [p] phoneme is
considered a “plosive consonant” or “oral occlusive,” meaning that it is created
when the vocal tract is suddenly blocked (by the lips in the case of [p]) so that
air no longer passes. It is also necessarily quick. The [E] vowel is caused by
resonances of the vocal cord and open mouth. It is considered a “long vowel,”
meaning that it persists for a much longer period of time than consonants such as
[t] and [p]; however, its duration can be quite variable. The [s] phoneme is
known as a “sibilant consonant,” and is caused by the passage of air against the
edges of the teeth, which are held close together. Its duration is typically shorter
than that of a long vowel such as [E], but it is also variable (in other words, the
[s] can be said quickly or you can drag it out).

In our work in speech recognition, we found that it is necessary to encode

this type of information in order to recognize speech patterns. For example, the
words “step” and “steep” are very similar. Although the [e] phoneme in “step”
and the [E] in “steep” are somewhat different vowel sounds (in that they have
different resonant frequencies), it is not reliable to distinguish these two words
based on these often confusable vowel sounds. It is much more reliable to
consider the observation that the [e] in “step” is relatively brief compared with
the [E] in “steep.”

We can encode this type of information with two numbers for each input:
the expected size and the degree of variability of that size. In our “steep”
example, [t] and [p] would both have a very short expected duration as well as a
small expected variability (that is, we do not expect to hear long t’s and p’s). The
[s] sound would have a short expected duration but a larger variability because it
is possible to drag it out. The [E] sound has a long expected duration as well as a
high degree of variability.

In our speech examples, the “size” parameter refers to duration, but time is
only one possible dimension. In our work in character recognition, we found that
comparable spatial information was important in order to recognize printed
letters (for example the dot over the letter “i” is expected to be much smaller
than the portion under the dot). At much higher levels of abstraction, the
neocortex will deal with patterns with all sorts of continuums, such as levels of
attractiveness, irony, happiness, frustration, and myriad others. We can draw
similarities across rather diverse continuums, as Darwin did when he related the
physical size of geological canyons to the amount of differentiation among
species.

In a biological brain, the source of these parameters comes from the brain’s
own experience. We are not born with an innate knowledge of phonemes; indeed
different languages have very different sets of them. This implies that multiple
examples of a pattern are encoded in the learned parameters of each pattern
recognizer (as it requires multiple instances of a pattern to ascertain the expected
distribution of magnitudes of the inputs to the pattern). In some AI systems,
these types of parameters are hand-coded by experts (for example, linguists who
can tell us the expected durations of different phonemes, as I articulated above).
In my own work, we found that having an AI system discover these parameters
on its own from training data (similar to the way the brain does it) was a superior
approach. Sometimes we used a hybrid approach; that is, we primed the system
with the intuition of human experts (for the initial settings of the parameters) and
then had the AI system automatically refine these estimates using a learning
process from real examples of speech.

What the pattern recognition module is doing is computing the probability

(that is, the likelihood based on all of its previous experience) that the pattern
that it is responsible for recognizing is in fact currently represented by its active
inputs. Each particular input to the module is active if the corresponding lower-
level pattern recognizer is firing (meaning that that lower-level pattern was
recognized). Each input also encodes the observed size (on some appropriate
dimension such as temporal duration or physical magnitude or some other
continuum) so that the size can be compared (with the stored size parameters for
each input) by the module in computing the overall probability of the pattern.

How does the brain (and how can an AI system) compute the overall
probability that the pattern (that the module is responsible for recognizing) is
present given (1) the inputs (each with an observed size), (2) the stored
parameters on size (the expected size and the variability of size) for each input,
and (3) the parameters of the importance of each input? In the 1980s and 1990s,
I and others pioneered a mathematical method called hierarchical hidden Markov
models for learning these parameters and then using them to recognize
hierarchical patterns. We used this technique in the recognition of human speech
as well as the understanding of natural language. I describe this approach further
in chapter 7 .

Getting back to the flow of recognition from one level of pattern
recognizers to the next, in the above example we see the information flow up the
conceptual hierarchy from basic letter features to letters to words. Recognitions
will continue to flow up from there to phrases and then more complex language
structures. If we go up several dozen more levels, we get to higher-level
concepts like irony and envy. Even though every pattern recognizer is working
simultaneously, it does take time for recognitions to move upward in this
conceptual hierarchy. Traversing each level takes between a few hundredths to a
few tenths of a second to process. Experiments have shown that a moderately
high-level pattern such as a face takes at least a tenth of a second. It can take as
long as an entire second if there are significant distortions. If the brain were
sequential (like conventional computers) and was performing each pattern
recognition in sequence, it would have to consider every possible low-level
pattern before moving on to the next level. Thus it would take many millions of
cycles just to go through each level. That is exactly what happens when we
simulate these processes on a computer. Keep in mind, however, that computers
process millions of times faster than our biological circuits.

A very important point to note here is that information flows down the
conceptual hierarchy as well as up. If anything, this downward flow is even
more significant. If, for example, we are reading from left to right and have
already seen and recognized the letters “A,” “P,” “P,” and “L,” the “APPLE”

recognizer will predict that it is likely to see an “E” in the next position. It will
send a signal down to the “E” recognizer saying, in effect, “Please be aware that
there is a high likelihood that you will see your ‘E’ pattern very soon, so be on
the lookout for it.” The “E” recognizer then adjusts its threshold such that it is
more likely to recognize an “E.” So if an image appears next that is vaguely like
an “E,” but is perhaps smudged such that it would not have been recognized as
an “E” under “normal” circumstances, the “E” recognizer may nonetheless
indicate that it has indeed seen an “E,” since it was expected.

The neocortex is, therefore, predicting what it expects to encounter.
Envisaging the future is one of the primary reasons we have a neocortex. At the
highest conceptual level, we are continually making predictions—who is going
to walk through the door next, what someone is likely to say next, what we
expect to see when we turn the corner, the likely results of our own actions, and
so on. These predictions are constantly occurring at every level of the neocortex
hierarchy. We often misrecognize people and things and words because our
threshold for confirming an expected pattern is too low.

In addition to positive signals, there are also negative or inhibitory signals
which indicate that a certain pattern is less likely to exist. These can come from
lower conceptual levels (for example, the recognition of a mustache will inhibit
the likelihood that a person I see in the checkout line is my wife), or from a
higher level (for example, I know that my wife is on a trip, so the person in the
checkout line can’t be she). When a pattern recognizer receives an inhibitory
signal, it raises the recognition threshold, but it is still possible for the pattern to
fire (so if the person in line really is her, I may still recognize her).

The Nature of the Data Flowing into a Neocortical Pattern
Recognizer

Let’s consider further what the data for a pattern looks like. If the pattern is a
face, the data exists in at least two dimensions. We cannot say that the eyes
necessarily come first, followed by the nose, and so on. The same thing is true
for most sounds. A musical piece has at least two dimensions. There may be
more than one instrument and/or voice making sounds at the same time.
Moreover, a single note of a complex instrument such as the piano consists of
multiple frequencies. A single human voice consists of varying levels of energy
in dozens of different frequency bands simultaneously. So a pattern of sound
may be complex at any one instant, and these complex instants stretch out over
time. Tactile inputs are also two-dimensional, since the skin is a two-dimensional
sense organ, and such patterns may change over the third dimension of time.

So it would seem that the input to a neocortex pattern processor must
comprise two- if not three-dimensional patterns. However, we can see in the
structure of the neocortex that the pattern inputs are only one-dimensional lists.
All of our work in the field of creating artificial pattern recognition systems
(such as speech recognition and visual recognition systems) demonstrates that
we can (and did) represent two- and three-dimensional phenomena with such
one-dimensional lists. I’ll describe how these methods work in chapter 7 . but for
now we can proceed with the understanding that the input to each pattern
processor is a one-dimensional list, even though the pattern itself may inherently
reflect more than one dimension.

We should factor in at this point the insight that the patterns we have
learned to recognize (for example, a specific dog or the general idea of a “dog,”
a musical note or a piece of music) are exactly the same mechanism that is the
basis for our memories. Our memories are in fact patterns organized as lists
(where each item in each list is another pattern in the cortical hierarchy) that we
have learned and then recognize when presented with the appropriate stimulus.
In fact, memories exist in the neocortex in order to be recognized.

The only exception to this is at the lowest possible conceptual level, in
which the input data to a pattern represents specific sensory information (for
example, image data from the optic nerve). Even this lowest level of pattern,
however, has been significantly transformed into simple patterns by the time it
reaches the cortex. The lists of patterns that constitute a memory are in forward

order, and we are able to remember our memories only in that order, hence the
difficulty we have in reversing our memories.

A memory needs to be triggered by another thought/memory (these are the
same thing). We can experience this mechanism of triggering when we are
perceiving a pattern. When we perceived “A,” “P,” “P,” and “L,” the “A P P L E”
pattern predicted that we would see an “E” and triggered the “E” pattern that it is
now expected. Our cortex is thereby “thinking” of seeing an “E” even before we
see it. If this particular interaction in our cortex has our attention, we will think
about “E” before we see it or even if we never see it. A similar mechanism
triggers old memories. Usually there is an entire chain of such links. Even if we
do have some level of awareness of the memories (that is, the patterns) that
triggered the old memory, memories (patterns) do not have language or image
labels. This is the reason why old memories may seem to suddenly jump into our
awareness. Having been buried and not activated for perhaps years, they need a
trigger in the same way that a Web page needs a Web link to be activated. And
just as a Web page can become “orphaned” because no other page links to it, the
same thing can happen to our memories.

Our thoughts are largely activated in one of two modes, undirected and
directed, both of which use these same cortical links. In the undirected mode, we
let the links play themselves out without attempting to move them in any
particular direction. Some forms of meditation (such as Transcendental
Meditation, which I practice) are based on letting the mind do exactly this.
Dreams have this quality as well.

In directed thinking we attempt to step through a more orderly process of
recalling a memory (a story, for example) or solving a problem. This also
involves stepping through lists in our neocortex, but the less structured flurry of
undirected thought will also accompany the process. The full content of our
thinking is therefore very disorderly, a phenomenon that James Joyce illuminated
in his “stream of consciousness” novels.

As you think through the memories/stories/patterns in your life, whether
they involve a chance encounter with a mother with a baby carriage and baby on
a walk or the more important narrative of how you met your spouse, your
memories consist of a sequence of patterns. Because these patterns are not
labeled with words or sounds or pictures or videos, when you try to recall a
significant event, you will essentially be reconstructing the images in your mind,
because the actual images do not exist.

If we were to “read” the mind of someone and peer at exactly what is going
on in her neocortex, it would be very difficult to interpret her memories, whether
we were to take a look at patterns that are simply stored in the neocortex waiting

to be triggered or those that have been triggered and are currently being
experienced as active thoughts. What we would “see” is the simultaneous
activation of millions of pattern recognizers. A hundredth of a second later, we
would see a different set of a comparable number of activated pattern
recognizers. Each such pattern would be a list of other patterns, and each of
those patterns would be a list of other patterns, and so on until we reached the
most elementary simple patterns at the lowest level. It would be extremely
difficult to interpret what these higher-level patterns meant without actually
copying all of the information at every level into our own cortex. Thus each
pattern in our neocortex is meaningful only in light of all the information carried
in the levels below it. Moreover, other patterns at the same level and at higher
levels are also relevant in interpreting a particular pattern because they provide
context. True mind reading, therefore, would necessitate not just detecting the
activations of the relevant axons in a person’s brain, but examining essentially
her entire neocortex with all of its memories to understand these activations.

As we experience our own thoughts and memories, we “know” what they
mean, but they do not exist as readily explainable thoughts and recollections. If
we want to share them with others, we need to translate them into language. This
task is also accomplished by the neocortex, using pattern recognizers trained
with patterns that we have learned for the purpose of using language. Language
is itself highly hierarchical and evolved to take advantage of the hierarchical
nature of the neocortex, which in turn reflects the hierarchical nature of reality.
The innate ability of humans to learn the hierarchical structures in language that
Noam Chomsky wrote about reflects the structure of the neocortex. In a 2002
paper he coauthored, Chomsky cites the attribute of “recursion” as accounting
for the unique language faculty of the human species;- Recursion, according to
Chomsky, is the ability to put together small parts into a larger chunk, and then
use that chunk as a part in yet another structure, and to continue this process
iteratively. In this way we are able to build the elaborate structures of sentences
and paragraphs from a limited set of words. Although Chomsky was not
explicitly referring here to brain structure, the capability he is describing is
exactly what the neocortex does.

Lower species of mammals largely use up their neocortex with the
challenges of their particular lifestyles. The human species acquired additional
capacities by having grown substantially more cortex to handle spoken and
written language. Some people have learned such skills better than others. If we
have told a particular story many times, we will begin to actually learn the
sequence of language that describes the story as a series of separate sequences.
Even in this case our memory is not a strict sequence of words, but rather of

language structures that we need to translate into specific word sequences each
time we deliver the story. That is why we tell a story a bit differently each time
we share it (unless we learn the exact word sequence as a pattern).

For each of these descriptions of specific thought processes, we also need to
consider the issue of redundancy. As I mentioned, we don’t have a single pattern
representing the important entities in our lives, whether those entities constitute
sensory categories, language concepts, or memories of events. Every important
pattern—at every level—is repeated many times. Some of these recurrences
represent simple repetitions, whereas many represent different perspectives and
vantage points. This is a principal reason why we can recognize a familiar face
from various orientations and under a range of lighting conditions. Each level up
the hierarchy has substantial redundancy, allowing sufficient variability that is
consistent with that concept.

So if we were to imagine examining your neocortex when you were looking
at a particular loved one, we would see a great many firings of the axons of the
pattern recognizers at every level, from the basic level of primitive sensory
patterns up to many different patterns representing that loved one’s image. We
would also see massive numbers of firings representing other aspects of the
situation, such as that person’s movements, what she is saying, and so on. So if
the experience seems much richer than just an orderly trip up a hierarchy of
features, it is.

A computer simulation of the firings of many simultaneous pattern

recognizers in the neocortex.

But the basic mechanism of going up a hierarchy of pattern recognizers in
which each higher conceptual level represents a more abstract and more
integrated concept remains valid. The flow of information downward is even
greater, as each activated level of recognized pattern sends predictions to the
next lower-level pattern recognizer of what it is likely to be encountering next.
The apparent lushness of human experience is a result of the fact that all of the
hundreds of millions of pattern recognizers in our neocortex are considering
their inputs simultaneously.

In chapter 5 I’ll discuss the flow of information from touch, vision, hearing,
and other sensory organs into the neocortex. These early inputs are processed by
cortical regions that are devoted to relevant types of sensory input (although
there is enormous plasticity in the assignment of these regions, reflecting the
basic uniformity of function in the neocortex). The conceptual hierarchy
continues above the highest concepts in each sensory region of the neocortex.
The cortical association areas integrate input from the different sensory inputs.
When we hear something that perhaps sounds like our spouse’s voice, and then
see something that is perhaps indicative of her presence, we don’t engage in an
elaborate process of logical deduction; rather, we instantly perceive that our
spouse is present from the combination of these sensory recognitions. We
integrate all of the germane sensory and perceptual cues—perhaps even the
smell of her perfume or his cologne—as one multilevel perception.

At a conceptual level above the cortical sensory association areas, we are
capable of dealing with—perceiving, remembering, and thinking about—even
more abstract concepts. At the highest level we recognize patterns such as that’s
funny, or she’s pretty, or that’s ironic, and so on. Our memories include these
abstract recognition patterns as well. For example, we might recall that we were
taking a walk with someone and that she said something funny, and we laughed,
though we may not remember the actual joke itself. The memory sequence for
that recollection has simply recorded the perception of humor but not the precise
content of what was funny.

In the previous chapter I noted that we can often recognize a pattern even
though we don’t recognize it well enough to be able to describe it. For example,
I believe I could pick out a picture of the woman with the baby carriage whom I
saw earlier today from among a group of pictures of other women, despite the
fact that I am unable to actually visualize her and cannot describe much specific
about her. In this case my memory of her is a list of certain high-level features.
These features do not have language or image labels attached to them, and they
are not pixel images, so while I am able to think about her, I am unable to

describe her. However, if I am presented with a picture of her, I can process the
image, which results in the recognition of the same high-level features that were
recognized the first time I saw her. I would be able to thereby determine that the
features match and thus confidently pick out her picture.

Even though I saw this woman only once on my walk, there are probably
already multiple copies of her pattern in my neocortex. However, if I don’t think
about her for a given period of time, then these pattern recognizers will become
reassigned to other patterns. That is why memories grow dimmer with time: The
amount of redundancy becomes reduced until certain memories become extinct.
However, now that I have memorialized this particular woman by writing about
her here, I probably won’t forget her so easily.

Autoassociation and Invariance

In the previous chapter I discussed how we can recognize a pattern even if the
entire pattern is not present, and also if it is distorted. The first capability is
called autoassociation: the ability to associate a pattern with a part of itself. The
structure of each pattern recognizer inherently supports this capability.

As each input from a lower-level pattern recognizer flows up to a higher-
level one, the connection can have a “weight,” indicating how important that
particular element in the pattern is. Thus the more significant elements of a
pattern are more heavily weighted in considering whether that pattern should
trigger as “recognized.” Lincoln’s beard, Elvis’s sideburns, and Einstein’s
famous tongue gesture are likely to have high weights in the patterns we’ve
learned about the appearance of these iconic figures. The pattern recognizer
computes a probability that takes the importance parameters into account. Thus
the overall probability is lower if one or more of the elements is missing, though
the threshold of recognition may nonetheless be met. As I pointed out, the
computation of the overall probability (that the pattern is present) is more
complicated than a simple weighted sum in that the size parameters also need to
be considered.

If the pattern recognizer has received a signal from a higher-level
recognizer that its pattern is “expected,” then the threshold is effectively lowered
(that is, made easier to achieve). Alternatively, such a signal may simply add to
the total of the weighted inputs, thereby compensating for a missing element.
This happens at every level, so that a pattern such as a face that is several levels
up from the bottom may be recognized even with multiple missing features.

The ability to recognize patterns even when aspects of them are
transformed is called feature invariance, and is dealt with in four ways. First,
there are global transformations that are accomplished before the neocortex
receives sensory data. We will discuss the voyage of sensory data from the eyes,
ears, and skin in the section “The Sensory Pathway” on page 94 .

The second method takes advantage of the redundancy in our cortical
pattern memory. Especially for important items, we have learned many different
perspectives and vantage points for each pattern. Thus many variations are
separately stored and processed.

The third and most powerful method is the ability to combine two lists. One
list can have a set of transformations that we have learned may apply to a certain

category of pattern; the cortex will apply this same list of possible changes to
another pattern. That is how we understand such language phenomena as
metaphors and similes.

For example, we have learned that certain phonemes (the basic sounds of
language) may be missing in spoken speech (for example, “gom”’). If we then
learn a new spoken word (for example, “driving”), we will be able to recognize
that word if one of its phonemes is missing even if we have never experienced
that word in that form before, because we have become familiar with the general
phenomenon of certain phonemes being omitted. As another example, we may
learn that a particular artist likes to emphasize (by making larger) certain
elements of a face, such as the nose. We can then identify a face with which we
are familiar to which that modification has been applied even if we have never
seen that modification on that face. Certain artistic modifications emphasize the
very features that are recognized by our pattern recognition-based neocortex. As
mentioned, that is precisely the basis of caricature.

The fourth method derives from the size parameters that allow a single
module to encode multiple instances of a pattern. For example, we have heard
the word “steep” many times. A particular pattern recognition module that is
recognizing this spoken word can encode these multiple examples by indicating
that the duration of [E] has a high expected variability. If all the modules for
words including [E] share a similar phenomenon, that variability could be
encoded in the models for [E] itself. However, different words incorporating [E]
(or many other phonemes) may have different amounts of expected variability.
For example, the word “peak” is likely not to have the [E] phoneme as drawn out
as in the word “steep.”

Learning

Are we not ourselves creating our successors in the supremacy of the earth?
Daily adding to the beauty and delicacy of their organization, daily giving
them greater skill and supplying more and more of that self-regulating self¬
acting power which will be better than any intellect?

—Samuel Butler, 1871

The principal activities of brains are making changes in themselves.

—Marvin Minsky, The Society of Mind

So far we have examined how we recognize (sensory and perceptual) patterns
and recall sequences of patterns (our memory of things, people, and events).
However, we are not born with a neocortex filled with any of these patterns. Our
neocortex is virgin territory when our brain is created. It has the capability of
learning and therefore of creating connections between its pattern recognizers,
but it gains those connections from experience.

This learning process begins even before we are born, occurring
simultaneously with the biological process of actually growing a brain. A fetus
already has a brain at one month, although it is essentially a reptile brain, as the
fetus actually goes through a high-speed re-creation of biological evolution in
the womb. The natal brain is distinctly a human brain with a human neocortex
by the time it reaches the third trimester of pregnancy. At this time the fetus is
having experiences, and the neocortex is learning. She can hear sounds,
especially her mother’s heartbeat, which is one likely reason that the rhythmic
qualities of music are universal to human culture. Every human civilization ever
discovered has had music as part of its culture, which is not the case with other
art forms, such as pictorial art. It is also the case that the beat of music is
comparable to our heart rate. Music beats certainly vary—otherwise music
would not keep our interest—but heartbeats vary also. An overly regular
heartbeat is actually a symptom of a diseased heart. The eyes of a fetus are
partially open twenty-six weeks after conception, and are fully open most of the
time by twenty-eight weeks after conception. There may not be much to see
inside the womb, but there are patterns of light and dark that the neocortex

begins to process.

So while a newborn baby has had a bit of experience in the womb, it is
clearly limited. The neocortex may also learn from the old brain (a topic I
discuss in chapter 5 ). but in general at birth the child has a lot to learn—
everything from basic primitive sounds and shapes to metaphors and sarcasm.

Learning is critical to human intelligence. If we were to perfectly model
and simulate the human neocortex (as the Blue Brain Project is attempting to do)
and all of the other brain regions that it requires to function (such as the
hippocampus and thalamus), it would not be able to do very much—in the same
way that a newborn infant cannot do much (other than to be cute, which is
definitely a key survival adaptation).

Learning and recognition take place simultaneously. We start learning
immediately, and as soon as we’ve learned a pattern, we immediately start
recognizing it. The neocortex is continually trying to make sense of the input
presented to it. If a particular level is unable to fully process and recognize a
pattern, it gets sent to the next higher level. If none of the levels succeeds in
recognizing a pattern, it is deemed to be a new pattern. Classifying a pattern as
new does not necessarily mean that every aspect of it is new. If we are looking at
the paintings of a particular artist and see a cat’s face with the nose of an
elephant, we will be able to identify each of the distinctive features but will
notice that this combined pattern is something novel, and are likely to remember
it. Higher conceptual levels of the neocortex, which understand context—for
example, the circumstance that this picture is an example of a particular artist’s
work and that we are attending an opening of a showing of new paintings by that
artist—will note the unusual combination of patterns in the cat-elephant face but
will also include these contextual details as additional memory patterns.

New memories such as the cat-elephant face are stored in an available
pattern recognizer. The hippocampus plays a role in this process, and we’ll
discuss what is known about the actual biological mechanisms in the following
chapter. For the purposes of our neocortex model, it is sufficient to say that
patterns that are not otherwise recognized are stored as new patterns and are
appropriately connected to the lower-level patterns that form them. The cat-
elephant face, for example, will be stored in several different ways: The novel
arrangement of facial parts will be stored as well as contextual memories that
include the artist, the situation, and perhaps the fact that we laughed when we
first saw it.

Memories that are successfully recognized may also result in the creation of
a new pattern to achieve greater redundancy. If patterns are not perfectly
recognized, they are likely to be stored as reflecting a different perspective of the

item that was recognized.

What, then, is the overall method for determining what patterns get stored?
In mathematical terms, the problem can be stated as follows: Using the available
limits of pattern storage, how do we optimally represent the input patterns that
have thus far been presented? While it makes sense to allow for a certain amount
of redundancy, it would not be practical to fill up the entire available storage area
(that is, the entire neocortex) with repeated patterns, as that would not allow for
a sufficient diversity of patterns. A pattern such as the [E] phoneme in spoken
words is something we have experienced countless times. It is a simple pattern
of sound frequencies and it undoubtedly enjoys significant redundancy in our
neocortex. We could fill up our entire neocortex with repeated patterns of the [E]
phoneme. There is a limit, however, to useful redundancy, and a common pattern
such as this clearly has reached it.

There is a mathematical solution to this optimization problem called linear
programming, which solves for the best possible allocation of limited resources
(in this case, a limited number of pattern recognizers) that would represent all of
the cases on which the system has trained. Linear programming is designed for
systems with one-dimensional inputs, which is another reason why it is optimal
to represent the input to each pattern recognition module as a linear string of
inputs. We can use this mathematical approach in a software system, and though
an actual brain is further constrained by the physical connections it has available
that it can adapt between pattern recognizers, the method is nonetheless similar.

An important implication of this optimal solution is that experiences that
are routine are recognized but do not result in a permanent memory’s being
made. With regard to my walk, I experienced millions of patterns at every level,
from basic visual edges and shadings to objects such as lampposts and mailboxes
and people and animals and plants that I passed. Almost none of what I
experienced was unique, and the patterns that I recognized had long since
reached their optimal level of redundancy. The result is that I recall almost
nothing from this walk. The few details that I do remember are likely to get
overwritten with new patterns by the time I take another few dozen walks—
except for the fact that I have now memorialized this particular walk by writing
about it.

One important point that applies to both our biological neocortex and
attempts to emulate it is that it is difficult to learn too many conceptual levels
simultaneously. We can essentially learn one or at most two conceptual levels at
a time. Once that learning is relatively stable, we can go on to learn the next
level. We may continue to fine-tune the learning in the lower levels, but our
learning focus is on the next level of abstraction. This is true at both the

beginning of life, as newborns struggle with basic shapes, and later in life, as we
struggle to learn new subject matter, one level of complexity at a time. We find
the same phenomenon in machine emulations of the neocortex. However, if they
are presented increasingly abstract material one level at a time, machines are
capable of learning just as humans do (although not yet with as many conceptual
levels).

The output of a pattern can feed back to a pattern at a lower level or even to
the pattern itself, giving the human brain its powerful recursive ability. An
element of a pattern can be a decision point based on another pattern. This is
especially useful for lists that compose actions—for example, getting another
tube of toothpaste if the current one is empty. These conditionals exist at every
level. As anyone who has attempted to program a procedure on a computer
knows, conditionals are vital to describing a course of action.

The Language of Thought

The dream acts as a safety-valve for the over-burdened brain.

—Sigmund Freud,

The Interpretation of Dreams, 1911

Brain: an apparatus with which we think we think.

—Ambrose Bierce, The Devil’s Dictionary

To summarize what we’ve learned so far about the way the neocortex works,
please refer to the diagram of the neocortical pattern recognition module on page

42.

a) Dendrites enter the module that represents the pattern. Even though
patterns may seem to have two- or three-dimensional qualities, they are
represented by a one-dimensional sequence of signals. The pattern must
be present in this (sequential) order for the pattern recognizer to be able
to recognize it. Each of the dendrites is connected ultimately to one or
more axons of pattern recognizers at a lower conceptual level that have
recognized a lower-level pattern that constitutes part of this pattern. For
each of these input patterns, there may be many lower-level pattern
recognizers that can generate the signal that the lower-level pattern has
been recognized. The necessary threshold to recognize the pattern may
be achieved even if not all of the inputs have signaled. The module
computes the probability that the pattern it is responsible for is present.
This computation considers the “importance” and “size” parameters (see
[f] below).

Note that some of the dendrites transmit signals into the module and
some out of the module. If all of the input dendrites to this pattern
recognizer are signaling that their lower-level patterns have been
recognized except for one or two, then this pattern recognizer will send a
signal down to the pattern recognizer(s) recognizing the lower-level
patterns that have not yet been recognized, indicating that there is a high
likelihood that that pattern will soon be recognized and that lower-level

recognizer(s) should be on the lookout for it.

b) When this pattern recognizer recognizes its pattern (based on all or most
of the input dendrite signals being activated), the axon (output) of this
pattern recognizer will activate. In turn, this axon can connect to an entire
network of dendrites connecting to many higher-level pattern recognizers
that this pattern is input to. This signal will transmit magnitude
information so that the pattern recognizers at the next higher conceptual
level can consider it.

c) If a higher-level pattern recognizer is receiving a positive signal from all
or most of its constituent patterns except for the one represented by this
pattern recognizer, then that higher-level recognizer might send a signal
down to this recognizer indicating that its pattern is expected. Such a
signal would cause this pattern recognizer to lower its threshold, meaning
that it would be more likely to send a signal on its axon (indicating that
its pattern is considered to have been recognized) even if some of its
inputs are missing or unclear.

d) Inhibitory signals from below would make it less likely that this pattern
recognizer will recognize its pattern. This can result from recognition of
lower-level patterns that are inconsistent with the pattern associated with
this pattern recognizer (for example, recognition of a mustache by a
lower-level recognizer would make it less likely that this image is “my
wife”).

e) Inhibitory signals from above would also make it less likely that this
pattern recognizer will recognize its pattern. This can result from a
higher-level context that is inconsistent with the pattern associated with
this recognizer.

f) For each input, there are stored parameters for importance, expected size,
and expected variability of size. The module computes an overall
probability that the pattern is present based on all of these parameters and
the current signals indicating which of the inputs are present and their
magnitudes. A mathematically optimal way to accomplish this is with a
technique called hidden Markov models. When such models are
organized in a hierarchy (as they are in the neocortex or in attempts to
simulate a neocortex), we call them hierarchical hidden Markov models.

Patterns triggered in the neocortex trigger other patterns. Partially complete
patterns send signals down the conceptual hierarchy; completed patterns send
signals up the conceptual hierarchy. These neocortical patterns are the language
of thought. Just like language, they are hierarchical, but they are not language
per se. Our thoughts are not conceived primarily in the elements of language,
although since language also exists as hierarchies of patterns in our neocortex,
we can have language-based thoughts. But for the most part, thoughts are
represented in these neocortical patterns.

As I discussed above, if we were able to detect the pattern activations in
someone’s neocortex, we would still have little idea what those pattern
activations meant without also having access to the entire hierarchy of patterns
above and below each activated pattern. That would pretty much require access
to that person’s entire neocortex. It is hard enough for us to understand the
content of our own thoughts, but understanding another person’s requires
mastering a neocortex different from our own. Of course we don’t yet have
access to someone else’s neocortex; we need instead to rely on her attempts to
express her thoughts into language (as well as other means such as gestures).
People’s incomplete ability to accomplish these communication tasks adds
another layer of complexity—it is no wonder that we misunderstand one another
as much as we do.

We have two modes of thinking. One is nondirected thinking, in which
thoughts trigger one another in a nonlogical way. When we experience a sudden
recollection of a memory from years or decades ago while doing something else,
such as raking the leaves or walking down the street, the experience is recalled—
as all memories are—as a sequence of patterns. We do not immediately visualize
the scene unless we can call upon a lot of other memories that enable us to
synthesize a more robust recollection. If we do visualize the scene in that way,
we are essentially creating it in our mind from hints at the time of recollection;
the memory itself is not stored in the form of images or visualizations. As I
mentioned earlier, the triggers that led this thought to pop into our mind may or
may not be evident. The sequence of relevant thoughts may have been
immediately forgotten. Even if we do remember it, it will be a nonlinear and
circuitous sequence of associations.

The second mode of thinking is directed thinking, which we use when we
attempt to solve a problem or formulate an organized response. For example, we
might be rehearsing in our mind something we plan to say to someone, or we
might be formulating a passage we want to write (in a book on the mind,
perhaps). As we think about tasks such as these, we have already broken down

each one into a hierarchy of subtasks. Writing a book, for example, involves
writing chapters; each chapter has sections; each section has paragraphs; each
paragraph contains sentences that express ideas; each idea has its configuration
of elements; each element and each relationship between elements is an idea that
needs to be articulated; and so on. At the same time, our neocortical structures
have learned certain rules that should be followed. If the task is writing, then we
should try to avoid unnecessary repetition; we should try to make sure that the
reader can follow what is being written; we should try to follow rules about
grammar and style; and so on. The writer needs therefore to build a model of the
reader in his mind, and that construct is hierarchical as well. In doing directed
thinking, we are stepping through lists in our neocortex, each of which expands
into extensive hierarchies of sublists, each with its own considerations. Keep in
mind that elements in a list in a neocortical pattern can include conditionals, so
our subsequent thoughts and actions will depend on assessments made as we go
through the process.

Moreover, each such directed thought will trigger hierarchies of undirected
thoughts. A continual storm of ruminations attends both our sensory experiences
and our attempts at directed thinking. Our actual mental experience is complex
and messy, made up of these lightning storms of triggered patterns, which
change about a hundred times a second.

The Language of Dreams

Dreams are examples of undirected thoughts. They make a certain amount of
sense because the phenomenon of one thought’s triggering another is based on
the actual linkages of patterns in our neocortex. To the extent that a dream does
not make sense, we attempt to fix it through our ability to confabulate. As I will
describe in chapter 9 . split-brain patients (whose corpus callosum, which
connects the two hemispheres of the brain, is severed or damaged) will
confabulate (make up) explanations with their left brain—which controls the
speech center—to explain what the right brain just did with input that the left
brain did not have access to. We confabulate all the time in explaining the
outcome of events. If you want a good example of this, just tune in to the daily
commentary on the movement of financial markets. No matter how the markets
perform, it’s always possible to come up with a good explanation for why it
happened, and such after-the-fact commentary is plentiful. Of course, if these
commentators really understood the markets, they wouldn’t have to waste their
time doing commentary.

The act of confabulating is of course also done in the neocortex, which is
good at coming up with stories and explanations that meet certain constraints.
We do that whenever we retell a story. We will fill in details that may not be
available or that we may have forgotten so that the story makes more sense. That
is why stories change over time as they are told over and over again by new
storytellers with perhaps different agendas. As spoken language led to written
language, however, we had a technology that could record a definitive version of
a story and prevent this sort of drift.

The actual content of a dream, to the extent that we remember it, is again a
sequence of patterns. These patterns represent constraints in a story; we then
confabulate a story that fits these constraints. The version of the dream that we
retell (even if only to ourselves silently) is this confabulation. As we recount a
dream we trigger cascades of patterns that fill in the actual dream as we
originally experienced it.

There is one key difference between dream thoughts and our thinking while
awake. One of the lessons we learn in life is that certain actions, even thoughts,
are not permissible in the real world. For example, we learn that we cannot
immediately fulfill our desires. There are rules against grabbing the money in the
cash register at a store, and constraints on interacting with a person to whom we

may be physically attracted. We also learn that certain thoughts are not
permissible because they are culturally forbidden. As we learn professional
skills, we learn the ways of thinking that are recognized and rewarded in our
professions, and thereby avoid patterns of thought that might betray the methods
and norms of that profession. Many of these taboos are worthwhile, as they
enforce social order and consolidate progress. However, they can also prevent
progress by enforcing an unproductive orthodoxy. Such orthodoxy is precisely
what Einstein left behind when he tried to ride a light beam with his thought
experiments.

Cultural rules are enforced in the neocortex with help from the old brain,
especially the amygdala. Every thought we have triggers other thoughts, and
some of them will relate to associated dangers. We learn, for example, that
breaking a cultural norm even in our private thoughts can lead to ostracism,
which the neocortex realizes threatens our well-being. If we entertain such
thoughts, the amygdala is triggered, and that generates fear, which generally
leads to terminating that thought.

In dreams, however, these taboos are relaxed, and we will often dream
about matters that are culturally, sexually, or professionally forbidden. It is as if
our brain realizes that we are not an actual actor in the world while dreaming.
Freud wrote about this phenomenon but also noted that we will disguise such
dangerous thoughts, at least when we attempt to recall them, so that the awake
brain continues to be protected from them.

Relaxing professional taboos turns out to be useful for creative problem
solving. I use a mental technique each night in which I think about a particular
problem before I go to sleep. This triggers sequences of thoughts that will
continue into my dreams. Once I am dreaming, I can think— dream —about
solutions to the problem without the burden of the professional restraints I carry
during the day. I can then access these dream thoughts in the morning while in
an in-between state of dreaming and being awake, sometimes referred to as
“lucid dreaming.

Freud also famously wrote about the ability to gain insight into a person’s
psychology by interpreting dreams. There is of course a vast literature on all
aspects of this theory, but the fundamental notion of gaining insight into
ourselves through examination of our dreams makes sense. Our dreams are
created by our neocortex, and thus their substance can be revealing of the
content and connections found there. The relaxation of the constraints on our
thinking that exist while we are awake is also useful in revealing neocortical
content that we otherwise would be unable to access directly. It is also
reasonable to conclude that the patterns that end up in our dreams represent

important matters to us and thereby clues in understanding our unresolved
desires and fears.

The Roots of the Model

As I mentioned above, I led a team in the 1980s and 1990s that developed the
technique of hierarchical hidden Markov models to recognize human speech and
understand natural-language statements. This work was the predecessor to
today’s widespread commercial systems that recognize and understand what we
are trying to tell them (car navigation systems that you can talk to, Siri on the
iPhone, Google Voice Search, and many others). The technique we developed
had substantially all of the attributes that I describe in the PRTM. It included a
hierarchy of patterns with each higher level being conceptually more abstract
than the one below it. For example, in speech recognition the levels included
basic patterns of sound frequency at the lowest level, then phonemes, then words
and phrases (which were often recognized as if they were words). Some of our
speech recognition systems could understand the meaning of natural-language
commands, so yet higher levels included such structures as noun and verb
phrases. Each pattern recognition module could recognize a linear sequence of
patterns from a lower conceptual level. Each input had parameters for
importance, size, and variability of size. There were “downward” signals
indicating that a lower-level pattern was expected. I discuss this research in more
detail in chapter 7 .

In 2003 and 2004, PalmPilot inventor Jeff Hawkins and Dileep George
developed a hierarchical cortical model called hierarchical temporal memory.
With science writer Sandra Blakeslee, Hawkins described this model eloquently
in their book On Intelligence. Hawkins provides a strong case for the uniformity
of the cortical algorithm and its hierarchical and list-based organization. There
are some important differences between the model presented in On Intelligence
and what I present in this book. As the name implies, Hawkins is emphasizing
the temporal (time-based) nature of the constituent lists. In other words, the
direction of the lists is always forward in time. His explanation for how the
features in a two-dimensional pattern such as the printed letter “A” have a
direction in time is predicated on eye movement. He explains that we visualize
images using saccades, which are very rapid movements of the eye of which we
are unaware. The information reaching the neocortex is therefore not a two-
dimensional set of features but rather a time-ordered list. While it is true that our
eyes do make very rapid movements, the sequence in which they view the
features of a pattern such as the letter “A” does not always occur in a consistent

temporal order. (For example, eye saccades will not always register the top
vertex in “A” before its bottom concavity.) Moreover, we can recognize a visual
pattern that is presented for only a few tens of milliseconds, which is too short a
period of time for eye saccades to scan it. It is true that the pattern recognizers in
the neocortex store a pattern as a list and that the list is indeed ordered, but the
order does not necessarily represent time. That is often indeed the case, but it
may also represent a spatial or higher-level conceptual ordering as I discussed
above.

The most important difference is the set of parameters that I have included
for each input into the pattern recognition module, especially the size and size
variability parameters. In the 1980s we actually tried to recognize human speech
without this type of information. This was motivated by linguists’ telling us that
the duration information was not especially important. This perspective is
illustrated by dictionaries that write out the pronunciation of each word as a
string of phonemes, for example the word “steep” as [s] [t] [E] [p], with no
indication of how long each phoneme is expected to last. The implication is that
if we create programs to recognize phonemes and then encounter this particular
sequence of four phonemes (in a spoken utterance), we should be able to
recognize that spoken word. The system we built using this approach worked to
some extent but not well enough to deal with such attributes as a large
vocabulary, multiple speakers, and words spoken continuously without pauses.
When we used the technique of hierarchical hidden Markov models in order to
incorporate the distribution of magnitudes of each input, performance soared.

CHAPTER 4

THE BIOLOGICAL
NEOCORTEX

Because important things go in a case, you’ve got a skull for your brain, a
plastic sleeve for your comb, and a wallet for your money.

—George Costanza, in “The Reverse Peephole” episode of Seinfeld

Now, for the first time, we are observing the brain at work in a global
manner with such clarity that we should be able to discover the overall
programs behind its magnificent powers.

—J. G. Taylor, B. Horwitz, and K. J. Friston

The mind, in short, works on the data it receives very much as a sculptor
works on his block of stone. In a sense the statue stood there from eternity.
But there were a thousand different ones beside it, and the sculptor alone is
to thank for having extricated this one from the rest. Just so the world of
each of us, howsoever different our several views of it may be, all lay
embedded in the primordial chaos of sensations, which gave the mere
matter to the thought of all of us indifferently. We may, if we like, by our
reasonings unwind things back to that black and jointless continuity of
space and moving clouds of swarming atoms which science calls the only
real world. But all the while the world we feel and live in will be that which
our ancestors and we, by slowly cumulative strokes of choice, have
extricated out of this, like sculptors, by simply rejecting certain portions of
the given stuff. Other sculptors, other statues from the same stone! Other
minds, other worlds from the same monotonous and inexpressive chaos!
My world is but one in a million alike embedded, alike real to those who
may abstract them. How different must be the worlds in the consciousness
of ant, cuttle-fish, or crab!

—William James

Is intelligence the goal, or even a goal, of biological evolution? Steven Pinker
writes, “We are chauvinistic about our brains, thinking them to be the goal of
evolution,”- and goes on to argue that “that makes no sense.... Natural selection
does nothing even close to striving for intelligence. The process is driven by
differences in the survival and reproduction rates of replicating organisms in a
particular environment. Over time, the organisms acquire designs that adapt
them for survival and reproduction in that environment, period; nothing pulls
them in any direction other than success there and then.” Pinker concludes that
“life is a densely branching bush, not a scale or a ladder, and living organisms
are at the tips of branches, not on lower rungs.”

With regard to the human brain, he questions whether the “benefits
outweigh the costs.” Among the costs, he cites that “the brain [is] bulky. The
female pelvis barely accommodates a baby’s outsized head. That design
compromise kills many women during childbirth and requires a pivoting gait
that makes women biomechanically less efficient walkers than men. Also a
heavy head bobbing around on a neck makes us more vulnerable to fatal injuries
in accidents such as falls.” He goes on to list additional shortcomings, including
the brain’s energy consumption, its slow reaction time, and the lengthy process
of learning.

While each of these statements is accurate on its face (although many of my
female friends are better walkers than I am), Pinker is missing the overall point
here. It is true that biologically, evolution has no specific direction. It is a search
method that indeed thoroughly fills out the “densely branching bush” of nature.
It is likewise true that evolutionary changes do not necessarily move in the
direction of greater intelligence—they move in all directions. There are many
examples of successful creatures that have remained relatively unchanged for
millions of years. (Alligators, for instance, date back 200 million years, and
many microorganisms go back much further than that.) But in the course of
thoroughly filling out myriad evolutionary branches, one of the directions it does
move in is toward greater intelligence. That is the relevant point for the purposes
of this discussion.

- Neocortex -

The neocortex covers the entire brain
with its convoluted thin surface and is
separated in two halves, connected
by the corpus callosum.

Sensorimotor Left Right

/ a rea hemisphere hemisphere

„ Visual
association

Frontal

lobe

Visual

Auditory

Auditory

association

Corpus

callosum

Thalamus

Nucleus

accumbens

Pituitary

gland

Cerebellum

Amygdala

Hippocampus

Physical layout of key regions of the brain.

The neocortex in different mammals.

Suppose we have a blue gas in a jar. When we remove the lid, there is no
message that goes out to all of the molecules of the gas saying, “Hey, guys, the
lid is off the jar; let’s head up toward the opening and out to freedom.” The
molecules just keep doing what they always do, which is to move every which
way with no seeming direction. But in the course of doing so, some of them near
the top will indeed move out of the jar, and over time most of them will follow
suit. Once biological evolution stumbled on a neural mechanism capable of
hierarchical learning, it found it to be immensely useful for evolution’s one
objective, which is survival. The benefit of having a neocortex became acute
when quickly changing circumstances favored rapid learning. Species of all
kinds—plants and animals—can learn to adapt to changing circumstances over
time, but without a neocortex they must use the process of genetic evolution. It
can take a great many generations—thousands of years—for a species without a
neocortex to learn significant new behaviors (or in the case of plants, other
adaptation strategies). The salient survival advantage of the neocortex was that it
could learn in a matter of days. If a species encounters dramatically changed
circumstances and one member of that species invents or discovers or just
stumbles upon (these three methods all being variations of innovation) a way to
adapt to that change, other individuals will notice, learn, and copy that method,
and it will quickly spread virally to the entire population. The cataclysmic

Cretaceous-Paleogene extinction event about 65 million years ago led to the
rapid demise of many non-neocortex-bearing species that could not adapt
quickly enough to a suddenly altered environment. This marked the turning point
for neocortex-capable mammals to take over their ecological niche. In this way,
biological evolution found that the hierarchical learning of the neocortex was so
valuable that this region of the brain continued to grow in size until it virtually
took over the brain of Homo sapiens.

Discoveries in neuroscience have established convincingly the key role
played by the hierarchical capabilities of the neocortex as well as offered
evidence for the pattern recognition theory of mind (PRTM). This evidence is
distributed among many observations and analyses, a portion of which I will
review here. Canadian psychologist Donald O. Hebb (1904-1985) made an
initial attempt to explain the neurological basis of learning. In 1949 he described
a mechanism in which neurons change physiologically based on their
experience, thereby providing a basis for learning and brain plasticity: “Let us
assume that the persistence or repetition of a reverberatory activity (or Trace’)
tends to induce lasting cellular changes that add to its stability.... When an axon
of cell A is near enough to excite a cell B and repeatedly or persistently takes
part in firing it, some growth process or metabolic change takes place in one or
both cells such that A’s efficiency, as one of the cells firing B, is increased.
This theory has been stated as “cells that fire together wire together” and has
become known as Hebbian learning. Aspects of Hebb’s theory have been
confirmed, in that it is clear that brain assemblies can create new connections
and strengthen them, based on their own activity. We can actually see neurons
developing such connections in brain scans. Artificial “neural nets” are based on
Hebb’s model of neuronal learning.

The central assumption in Hebb’s theory is that the basic unit of learning in
the neocortex is the neuron. The pattern recognition theory of mind that I
articulate in this book is based on a different fundamental unit: not the neuron
itself, but rather an assembly of neurons, which I estimate to number around a
hundred. The wiring and synaptic strengths within each unit are relatively stable
and determined genetically—that is, the organization within each pattern
recognition module is determined by genetic design. Learning takes place in the
creation of connections between these units, not within them, and probably in the
synaptic strengths of those interunit connections.

Recent support for the basic module of learning’s being a module of dozens
of neurons comes from Swiss neuroscientist Henry Markram (born in 1962),
whose ambitious Blue Brain Project to simulate the entire human brain I
describe in chapter 7 . In a 2011 paper he describes how while scanning and

analyzing actual mammalian neocortex neurons, he was “searching] for
evidence of Hebbian assemblies at the most elementary level of the cortex.”
What he found instead, he writes, were “elusive assemblies [whose] connectivity
and synaptic weights are highly predictable and constrained.” He concludes that
“these findings imply that experience cannot easily mold the synaptic
connections of these assemblies” and speculates that “they serve as innate, Lego¬
like building blocks of knowledge for perception and that the acquisition of
memories involves the combination of these building blocks into complex
constructs.” He continues:

Functional neuronal assemblies have been reported for decades, but
direct evidence of clusters of synaptically connected neurons...has been
missing.... Since these assemblies will all be similar in topology and
synaptic weights, not molded by any specific experience, we consider these
to be innate assemblies.... Experience plays only a minor role in
determining synaptic connections and weights within these assemblies....
Our study found evidence [of] innate Lego-like assemblies of a few dozen
neurons.... Connections between assemblies may combine them into super¬
assemblies within a neocortical layer, then in higher-order assemblies in a
cortical column, even higher-order assemblies in a brain region, and finally
in the highest possible order assembly represented by the whole brain....
Acquiring memories is very similar to building with Lego. Each assembly
is equivalent to a Lego block holding some piece of elementary innate
knowledge about how to process, perceive and respond to the world....
When different blocks come together, they therefore form a unique
combination of these innate percepts that represents an individual’s specific
knowledge and experience. -

The “Lego blocks” that Markram proposes are fully consistent with the
pattern recognition modules that I have described. In an e-mail communication,
Markram described these “Lego blocks” as “shared content and innate
knowledge.’” I would articulate that the purpose of these modules is to recognize
patterns, to remember them, and to predict them based on partial patterns. Note
that Markram’s estimate of each module’s containing “several dozen neurons” is
based only on layer V of the neocortex. Layer V is indeed neuron rich, but based
on the usual ratio of neuron counts in the six layers, this would translate to an
order of magnitude of about 100 neurons per module, which is consistent with
my estimates.

The consistent wiring and apparent modularity of the neocortex has been

noted for many years, but this study is the first to demonstrate the stability of
these modules as the brain undergoes its dynamic processes.

Another recent study, this one from Massachusetts General Hospital,
funded by the National Institutes of Health and the National Science Foundation
and published in a March 2012 issue of the journal Science, also shows a regular
structure of connections across the neocortex.- The article describes the wiring
of the neocortex as following a grid pattern, like orderly city streets: “Basically,
the overall structure of the brain ends up resembling Manhattan, where you have
a 2-D plan of streets and a third axis, an elevator going in the third dimension,”
wrote Van J. Wedeen, a Harvard neuroscientist and physicist and the head of the
study.

In a Science magazine podcast, Wedeen described the significance of the
research: “This was an investigation of the three-dimensional structure of the
pathways of the brain. When scientists have thought about the pathways of the
brain for the last hundred years or so, the typical image or model that comes to
mind is that these pathways might resemble a bowl of spaghetti—separate
pathways that have little particular spatial pattern in relation to one another.
Using magnetic resonance imaging, we were able to investigate this question
experimentally. And what we found was that rather than being haphazardly
arranged or independent pathways, we find that ah of the pathways of the brain
taken together fit together in a single exceedingly simple structure. They
basically look like a cube. They basically run in three perpendicular directions,
and in each one of those three directions the pathways are highly parallel to each
other and arranged in arrays. So, instead of independent spaghettis, we see that
the connectivity of the brain is, in a sense, a single coherent structure.”

Whereas the Markram study shows a module of neurons that repeats itself
across the neocortex, the Wedeen study demonstrates a remarkably orderly
pattern of connections between modules. The brain starts out with a very large
number of “connections-in-waiting” to which the pattern recognition modules
can hook up. Thus if a given module wishes to connect to another, it does not
need to grow an axon from one and a dendrite from the other to span the entire
physical distance between them. It can simply harness one of these axonal
connections-in-waiting and just hook up to the ends of the fiber. As Wedeen and
his colleagues write, “The pathways of the brain follow a base-plan established
by...early embryogenesis. Thus, the pathways of the mature brain present an
image of these three primordial gradients, physically deformed by
development.” In other words, as we learn and have experiences, the pattern
recognition modules of the neocortex are connecting to these preestablished
connections that were created when we were embryos.

There is a type of electronic chip called a field programmable gate array
(FPGA) that is based on a similar principle. The chip contains millions of
modules that implement logic functions along with connections-in-waiting. At
the time of use, these connections are either activated or deactivated (through
electronic signals) to implement a particular capability.

In the neocortex, those long-distance connections that are not used are
eventually pruned away, which is one reason why adapting a nearby region of
the neocortex to compensate for one that has become damaged is not quite as
effective as using the original region. According to the Wedeen study, the initial
connections are extremely orderly and repetitive, just like the modules
themselves, and their grid pattern is used to “guide connectivity” in the
neocortex. This pattern was found in all of the primate and human brains studied
and was evident across the neocortex, from regions that dealt with early sensory
patterns up to higher-level emotions. Wedeen’s Science journal article concluded
that the “grid structure of cerebral pathways was pervasive, coherent, and
continuous with the three principal axes of development.” This again speaks to a
common algorithm across all neocortical functions.

It has long been known that at least certain regions of the neocortex are
hierarchical. The best-studied region is the visual cortex, which is separated into
areas known as VI, V2, and MT (also known as V5). As we advance to higher
areas in this region (“higher” in the sense of conceptual processing, not
physically, as the neocortex is always just one pattern recognizer thick), the
properties that can be recognized become more abstract. VI recognizes very
basic edges and primitive shapes. V2 can recognize contours, the disparity of
images presented by each of the eyes, spatial orientation, and whether or not a
portion of the image is part of an object or the background.- Higher-level regions
of the neocortex recognize concepts such as the identity of objects and faces and
their movement. It has also long been known that communication through this
hierarchy is both upward and downward, and that signals can be both excitatory
and inhibitory. MIT neuroscientist Tomaso Poggio (born in 1947) has
extensively studied vision in the human brain, and his research for the last thirty-
five years has been instrumental in establishing hierarchical learning and pattern
recognition in the “early” (lowest conceptual) levels of the visual neocortex.

The highly regular grid structure of initial connections in the neocortex
found in a National Institutes of Health study.

Another view of the regular grid structure of neocortical connections.

The grid structure found in the neocortex is remarkably similar to what
is called crossbar switching, which is used in integrated circuits and circuit
boards.

Our understanding of the lower hierarchical levels of the visual neocortex is
consistent with the PRTM I described in the previous chapter , and observation of
the hierarchical nature of neocortical processing has recently extended far
beyond these levels. University of Texas neurobiology professor Daniel J.
Felleman and his colleagues traced the “hierarchical organization of the cerebral
cortex...[in] 25 neocortical areas,” which included both visual areas and higher-
level areas that combine patterns from multiple senses. What they found as they
went up the neocortical hierarchy was that the processing of patterns became
more abstract, comprised larger spatial areas, and involved longer time periods.
With every connection they found communication both up and down the
hierarchy.-

Recent research allows us to substantially broaden these observations to
regions well beyond the visual cortex and even to the association areas, which
combine inputs from multiple senses. A study published in 2008 by Princeton
psychology professor Uri Hasson and his colleagues demonstrates that the
phenomena observed in the visual cortex occur across a wide variety of
neocortical areas: “It is well established that neurons along the visual cortical
pathways have increasingly larger spatial receptive fields. This is a basic
organizing principle of the visual system.... Real-world events occur not only
over extended regions of space, but also over extended periods of time. We
therefore hypothesized that a hierarchy analogous to that found for spatial
receptive field sizes should also exist for the temporal response characteristics of

different brain regions.” This is exactly what they found, which enabled them to
conclude that “similar to the known cortical hierarchy of spatial receptive fields,
there is a hierarchy of progressively longer temporal receptive windows in the
human brain.”-

The most powerful argument for the universality of processing in the
neocortex is the pervasive evidence of plasticity (not just learning but
interchangeability): In other words, one region is able to do the work of other
regions, implying a common algorithm across the entire neocortex. A great deal
of neuroscience research has been focused on identifying which regions of the
neocortex are responsible for which types of patterns. The classical technique for
determining this has been to take advantage of brain damage from injury or
stroke and to correlate lost functionality with specific damaged regions. So, for
example, when we notice that someone with newly acquired damage to the
fusiform gyrus region suddenly has difficulty recognizing faces but is still able
to identify people from their voices and language patterns, we can hypothesize
that this region has something to do with face recognition. The underlying
assumption has been that each of these regions is designed to recognize and
process a particular type of pattern. Particular physical regions have become
associated with particular types of patterns, because under normal circumstances
that is how the information happens to flow. But when that normal flow of
information is disrupted for any reason, another region of the neocortex is able to
step in and take over.

Plasticity has been widely noted by neurologists, who observed that patients
with brain damage from an injury or a stroke can relearn the same skills in
another area of the neocortex. Perhaps the most dramatic example of plasticity is
a 2011 study by American neuroscientist Marina Bedny and her colleagues on
what happens to the visual cortex of congenitally blind people. The common
wisdom has been that the early layers of the visual cortex, such as VI and V2,
inherently deal with very low-level patterns (such as edges and curves), whereas
the frontal cortex (that evolutionarily new region of the cortex that we have in
our uniquely large foreheads) inherently deals with the far more complex and
subtle patterns of language and other abstract concepts. But as Bedny and her
colleagues found, “Humans are thought to have evolved brain regions in the left
frontal and temporal cortex that are uniquely capable of language processing.
However, congenitally blind individuals also activate the visual cortex in some
verbal tasks. We provide evidence that this visual cortex activity in fact reflects
language processing. We find that in congenitally blind individuals, the left
visual cortex behaves similarly to classic language regions.... We conclude that
brain regions that are thought to have evolved for vision can take on language

processing as a result of early experience.”—

Consider the implications of this study: It means that neocortical regions
that are physically relatively far apart, and that have also been considered
conceptually very different (primitive visual cues versus abstract language
concepts), use essentially the same algorithm. The regions that process these
disparate types of patterns can substitute for one another.

University of California at Berkeley neuroscientist Daniel E. Feldman
wrote a comprehensive 2009 review of what he called “synaptic mechanisms for
plasticity in the neocortex” and found evidence for this type of plasticity across
the neocortex. He writes that “plasticity allows the brain to learn and remember
patterns in the sensory world, to refine movements...and to recover function
after injury.” He adds that this plasticity is enabled by “structural changes
including formation, removal, and morphological remodeling of cortical
synapses and dendritic spines.”—

Another startling example of neocortical plasticity (and therefore of the
uniformity of the neocortical algorithm) was recently demonstrated by scientists
at the University of California at Berkeley. They hooked up implanted
microelectrode arrays to pick up brain signals specifically from a region of the
motor cortex of mice that controls the movement of their whiskers. They set up
their experiment so that the mice would get a reward if they controlled these
neurons to fire in a certain mental pattern but not to actually move their
whiskers. The pattern required to get the reward involved a mental task that their
frontal neurons would normally not do. The mice were nonetheless able to
perform this mental feat essentially by thinking with their motor neurons while
mentally decoupling them from controlling motor movements.— The conclusion
is that the motor cortex, the region of the neocortex responsible for coordinating
muscle movement, also uses the standard neocortical algorithm.

There are several reasons, however, why a skill or an area of knowledge
that has been relearned using a new area of the neocortex to replace one that has
been damaged will not necessarily be as good as the original. First, because it
took an entire lifetime to learn and perfect a given skill, relearning it in another
area of the neocortex will not immediately generate the same results. More
important, that new area of the neocortex has not just been sitting around waiting
as a standby for an injured region. It too has been carrying out vital functions,
and will therefore be hesitant to give up its neocortical patterns to compensate
for the damaged region. It can start by releasing some of the redundant copies of
its patterns, but doing so will subtly degrade its existing skills and does not free
up as much cortical space as the skills being relearned had used originally.

There is a third reason why plasticity has its limits. Since in most people
particular types of patterns will flow through specific regions (such as faces
being processed by the fusiform gyrus), these regions have become optimized
(by biological evolution) for those types of patterns. As I report in chapter 7 . we
found the same result in our digital neocortical developments. We could
recognize speech with our character recognition systems and vice versa, but the
speech systems were optimized for speech and similarly the character
recognition systems were optimized for printed characters, so there would be
some reduction in performance if we substituted one for the other. We actually
used evolutionary (genetic) algorithms to accomplish this optimization, a
simulation of what biology does naturally. Given that faces have been flowing
through the fusiform gyrus for most people for hundreds of thousands of years
(or more), biological evolution has had time to evolve a favorable ability to
process such patterns in that region. It uses the same basic algorithm, but it is
oriented toward faces. As Dutch neuroscientist Randal Koene wrote, “The
[neo]cortex is very uniform, each column or minicolumn can in principle do
what each other one can do.”—

Substantial recent research supports the observation that the pattern
recognition modules wire themselves based on the patterns to which they are
exposed. For example, neuroscientist Yi Zuo and her colleagues watched as new
“dendritic spines” formed connections between nerve cells as mice learned a
new skill (reaching through a slot to grab a seed).— Researchers at the Salk
Institute have discovered that this critical self-wiring of the neocortex modules is
apparently controlled by only a handful of genes. These genes and this method
of self-wiring are also uniform across the neocortex.—

Many other studies document these attributes of the neocortex, but let’s
summarize what we can observe from the neuroscience literature and from our
own thought experiments. The basic unit of the neocortex is a module of
neurons, which I estimate at around a hundred. These are woven together into
each neocortical column so that each module is not visibly distinct. The pattern
of connections and synaptic strengths within each module is relatively stable. It
is the connections and synaptic strengths between modules that represent
learning.

There are on the order of a quadrillion (10 15 ) connections in the neocortex,
yet only about 25 million bytes of design information in the genome (after
lossless compression),— so the connections themselves cannot possibly be
predetermined genetically. It is possible that some of this learning is the product
of the neocortex’s interrogating the old brain, but that still would necessarily

represent only a relatively small amount of information. The connections
between modules are created on the whole from experience (nurture rather than
nature).

The brain does not have sufficient flexibility so that each neocortical pattern
recognition module can simply link to any other module (as we can easily
program in our computers or on the Web)—an actual physical connection must
be made, composed of an axon connecting to a dendrite. We each start out with a
vast stockpile of possible neural connections. As the Wedeen study shows, these
connections are organized in a very repetitive and orderly manner. Terminal
connection to these axons-in-waiting takes place based on the patterns that each
neocortical pattern recognizer has recognized. Unused connections are
ultimately pruned away. These connections are built hierarchically, reflecting the
natural hierarchical order of reality. That is the key strength of the neocortex.

The basic algorithm of the neocortical pattern recognition modules is
equivalent across the neocortex from “low-level” modules, which deal with the
most basic sensory patterns, to “high-level” modules, which recognize the most
abstract concepts. The vast evidence of plasticity and the interchangeability of
neocortical regions is testament to this important observation. There is some
optimization of regions that deal with particular types of patterns, but this is a
second-order effect—the fundamental algorithm is universal.

Signals go up and down the conceptual hierarchy. A signal going up means,
“I’ve detected a pattern.” A signal going down means, “I’m expecting your
pattern to occur,” and is essentially a prediction. Both upward and downward
signals can be either excitatory or inhibitory.

Each pattern is itself in a particular order and is not readily reversed. Even
if a pattern appears to have multidimensional aspects, it is represented by a one¬
dimensional sequence of lower-level patterns. A pattern is an ordered sequence
of other patterns, so each recognizer is inherently recursive. There can be many
levels of hierarchy.

There is a great deal of redundancy in the patterns we learn, especially the
important ones. The recognition of patterns (such as common objects and faces)
uses the same mechanism as our memories, which are just patterns we have
learned. They are also stored as sequences of patterns—they are basically
stories. That mechanism is also used for learning and carrying out physical
movement in the world. The redundancy of patterns is what enables us to
recognize objects, people, and ideas even when they have variations and occur in
different contexts. The size and size variability parameters also allow the
neocortex to encode variation in magnitude against different dimensions
(duration in the case of sound). One way that these magnitude parameters could

be encoded is simply through multiple patterns with different numbers of
repeated inputs. So, for example, there could be patterns for the spoken word
“steep” with different numbers of the long vowel [E] repeated, each with the
importance parameter set to a moderate level indicating that the repetition of [E]
is variable. This approach is not mathematically equivalent to having the explicit
size parameters and does not work nearly as well in practice, but is one approach
to encoding magnitude. The strongest evidence we have for these parameters is
that they are needed in our AI systems to get accuracy levels that are near human
levels.

The summary above constitutes the conclusions we can draw from the
sampling of research results I have shared above as well as the sampling of
thought experiments I discussed earlier. I maintain that the model I have
presented is the only possible model that satisfies all of the constraints that the
research and our thought experiments have established.

Finally, there is one more piece of corroborating evidence. The techniques
that we have evolved over the past several decades in the field of artificial
intelligence to recognize and intelligently process real-world phenomena (such
as human speech and written language) and to understand natural-language
documents turn out to be mathematically similar to the model I have presented
above. They are also examples of the PRTM. The AI field was not explicitly
trying to copy the brain, but it nonetheless arrived at essentially equivalent
techniques.

CHAPTER 5

THE OLD BRAIN

I have an old brain but a terrific memory.

—A1 Lewis

Here we stand in the middle of this new world with our primitive brain,
attuned to the simple cave life, with terrific forces at our disposal, which we
are clever enough to release, but whose consequences we cannot
comprehend.

—Albert Szent-Gyorgyi

Our old brain—the one we had before we were mammals—has not
disappeared. Indeed it still provides much of our motivation in seeking
gratification and avoiding danger. These goals are modulated, however, by our
neocortex, which dominates the human brain in both mass and activity.

Animals used to live and survive without a neocortex, and indeed all
nonmammalian animals continue to do so today. We can view the human
neocortex as the great sublimator—thus our primitive motivation to avoid a large
predator may be transformed by the neocortex today into completing an
assignment to impress our boss; the great hunt may become writing a book on,
say, the mind; and pursuing reproduction may become gaining public
recognition or decorating your apartment. (Well, this last motivation is not
always so hidden.)

The neocortex is likewise good at helping us solve problems because it can
accurately model the world, reflecting its true hierarchical nature. But it is the
old brain that presents us with those problems. Of course, like any clever
bureaucracy, the neocortex often deals with the problems it is assigned by
redefining them. On that note, let’s review the information processing in the old
brain.

The Sensory Pathway

Pictures, propagated by motion along the fibers of the optic nerves in the
brain, are the cause of vision.

—Isaac Newton

Each of us lives within the universe—the prison—of his own brain.
Projecting from it are millions of fragile sensory nerve fibers, in groups
uniquely adapted to sample the energetic states of the world around us:
heat, light, force, and chemical composition. That is all we ever know of it
directly; all else is logical inference.

—Vernon Mountcastle 1

Although we experience the illusion of receiving high-resolution images from
our eyes, what the optic nerve actually sends to the brain is just a series of
outlines and clues about points of interest in our visual field. We then essentially
hallucinate the world from cortical memories that interpret a series of movies
with very low data rates that arrive in parallel channels. In a study published in
Nature, Frank S. Werblin, professor of molecular and cell biology at the
University of California at Berkeley, and doctoral student Boton Roska, MD,
showed that the optic nerve carries ten to twelve output channels, each of which
carries only a small amount of information about a given scene.- One group of
what are called ganglion cells sends information only about edges (changes in
contrast). Another group detects only large areas of uniform color, whereas a
third group is sensitive only to the backgrounds behind figures of interest.

The visual pathway in the brain.

“Even though we think we see the world so fully, what we are receiving is
really just hints, edges in space and time,” says Werblin. “These 12 pictures of
the world constitute all the information we will ever have about what’s out there,
and from these 12 pictures, which are so sparse, we reconstruct the richness of
the visual world. I’m curious how nature selected these 12 simple movies and
how it can be that they are sufficient to provide us with all the information we
seem to need.”

This data reduction is what in the AI field we call “sparse coding.” We have
found in creating artificial systems that throwing most of the input information
away and retaining only the most salient details provides superior results.
Otherwise the limited ability to process information in a neocortex (biological or
otherwise) gets overwhelmed.

Seven of the twelve low-data-rate “movies” sent by the optic nerve to
the brain.

The processing of auditory information from the human cochlea through the
subcortical regions and then through the early stages of the neocortex has been
meticulously modeled by Lloyd Watts and his research team at Audience, Inc.-
They have developed research technology that extracts 600 different frequency
bands (60 per octave) from sound. This comes much closer to the estimate of
3,000 bands extracted by the human cochlea (compared with commercial speech
recognition, which uses only 16 to 32 bands). Using two microphones and its
detailed (and high-spectral resolution) model of auditory processing, Audience
has created a commercial technology (with somewhat lower spectral resolution
than its research system) that effectively removes background noise from
conversations. This is now being used in many popular cell phones and is an
impressive example of a commercial product based on an understanding of how
the human auditory perceptual system is able to focus on one sound source of
interest.

The auditory pathway in the brain.

Inputs from the body (estimated at hundreds of megabits per second),
including that of nerves from the skin, muscles, organs, and other areas, stream
into the upper spinal cord. These messages involve more than just
communication about touch; in addition they carry information about
temperature, acid levels (for example, lactic acid in muscles), the movement of
food through the gastrointestinal tract, and many other signals. This data is
processed through the brain stem and midbrain. Key cells called lamina 1
neurons create a map of the body, representing its current state, not unlike the
displays used by flight controllers to track airplanes. From here the sensory data
heads to a mysterious region called the thalamus, which brings us to our next
topic.

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Cochlea

A simplified model of auditory processing in both the subcortical areas
(areas prior to the neocortex) and the neocortex, created by Audience, Inc.
Figure adapted from L. Watts, “Reverse-Engineering the Human Auditory
Pathway,” in J. Liu et al. (eds.), WCCI 2012 (Berlin: Springer-Verlag,
2012), p. 49.

The Thalamus

Everyone knows what attention is. It is the taking possession by the mind,
in clear and vivid form, of one out of what seem several simultaneously
possible objects or trains of thought. Focalization, concentration, of
consciousness, are of its essence. It implies withdrawal from some things in
order to deal effectively with others.

—William James

From the midbrain, sensory information then flows through a nut-sized region
called the posterior ventromedial nucleus (VMpo) of the thalamus, which
computes complex reactions to bodily states such as “this tastes terrible,” “what
a stench,” or “that light touch is stimulating.” The increasingly processed
information ends up at two regions of the neocortex called the insula. These
structures, the size of small fingers, are located on the left and right sides of the
neocortex. Dr. Arthur Craig of the Barrow Neurological Institute in Phoenix
describes the VMpo and the two insula regions as “a system that represents the
material me.”-

Among its other functions, the thalamus is considered a gateway for
preprocessed sensory information to enter the neocortex. In addition to the tactile
information flowing through the VMpo, processed information from the optic
nerve (which, as noted above, has already been substantially transformed) is sent
to a region of the thalamus called the lateral geniculate nucleus, which then
sends it on to the VI region of the neocortex. Information from the auditory
sense is passed through the medial geniculate nucleus of the thalamus en route to
the early auditory regions of the neocortex. All of our sensory data (except,
apparently, for the olfactory system, which uses the olfactory bulb instead)
passes through specific regions of the thalamus.

The most significant role of the thalamus, however, is its continual
communication with the neocortex. The pattern recognizers in the neocortex
send tentative results to the thalamus and receive responses principally using
both excitatory and inhibitory reciprocal signals from layer VI of each
recognizer. Keep in mind that these are not wireless messages, so that there
needs to be an extraordinary amount of actual wiring (in the form of axons)
mnning between all regions of the neocortex and the thalamus. Consider the vast
amount of real estate (in terms of the physical mass of connections required) for
the hundreds of millions of pattern recognizers in the neocortex to be constantly

checking in with the thalamus.-

So what are the hundreds of millions of neocortical pattern recognizers
talking to the thalamus about? It is apparently an important conversation,
because profound damage to the main region of the thalamus bilaterally can lead
to prolonged unconsciousness. A person with a damaged thalamus may still have
activity in his neocortex, in that the self-triggering thinking by association can
still work. But directed thinking—the kind that will get us out of bed, into our
car, and sitting at our desk at work—does not function without a thalamus. In a
famous case, twenty-one-year-old Karen Ann Quinlan suffered a heart attack and
respiratory failure and remained in an unresponsive, apparently vegetative state
for ten years. When she died, her autopsy revealed that her neocortex was
normal but her thalamus had been destroyed.

In order to play its key role in our ability to direct attention, the thalamus
relies on the structured knowledge contained in the neocortex. It can step
through a list (stored in the neocortex), enabling us to follow a train of thought
or follow a plan of action. We are apparently able to keep up to about four items
in our working memory at a time, two per hemisphere according to recent
research by neuroscientists at the MIT Picower Institute for Learning and
Memory.- The issue of whether the thalamus is in charge of the neocortex or
vice versa is far from clear, but we are unable to function without both.

The Hippocampus

Each brain hemisphere contains a hippocampus, a small region that looks like a
sea horse tucked in the medial temporal lobe. Its primary function is to
remember novel events. Since sensory information flows through the neocortex,
it is up to the neocortex to determine that an experience is novel in order to
present it to the hippocampus. It does so either by failing to recognize a
particular set of features (for example, a new face) or by realizing that an
otherwise familiar situation now has unique attributes (such as your spouse’s
wearing a fake mustache).

The hippocampus is capable of remembering these situations, although it
appears to do so primarily through pointers into the neocortex. So memories in
the hippocampus are also stored as lower-level patterns that were earlier
recognized and stored in the neocortex. For animals without a neocortex to
modulate sensory experiences, the hippocampus will simply remember the
information from the senses, although this will have undergone sensory
preprocessing (for example, the transformations performed by the optic nerve).

Although the hippocampus makes use of the neocortex (if a particular brain
has one) as its scratch pad, its memory (of pointers into the neocortex) is not
inherently hierarchical. Animals without a neocortex can accordingly remember
things using their hippocampus, but their recollections will not be hierarchical.

The capacity of the hippocampus is limited, so its memory is short-term. It
will transfer a particular sequence of patterns from its short-term memory to the
long-term hierarchical memory of the neocortex by playing this memory
sequence to the neocortex over and over again. We need, therefore, a
hippocampus in order to learn new memories and skills (although strictly motor
skills appear to use a different mechanism). Someone with damage to both
copies of her hippocampus will retain her existing memories but will not be able
to form new ones.

University of Southern California neuroscientist Theodore Berger and his
colleagues modeled the hippocampus of a rat and have successfully
experimented with implanting an artificial one. In a study reported in 2011, the
USC scientists blocked particular learned behaviors in rats with drugs. Using an
artificial hippocampus, the rats were able to quickly relearn the behavior. “Flip
the switch on, and the rats remember. Flip it off and the rats forget,” Berger
wrote, referring to his ability to control the neural implant remotely. In another

experiment the scientists allowed their artificial hippocampus to work alongside
the rats’ natural one. The result was that the ability of the rats to learn new
behaviors strengthened. “These integrated experimental modeling studies show
for the first time,” Berger explained, “that...a neural prosthesis capable of real¬
time identification and manipulation of the encoding process can restore and
even enhance cognitive mnemonic processes.”- The hippocampus is one of the
first regions damaged by Alzheimer’s, so one goal of this research is to develop
a neural implant for humans that will mitigate this first phase of damage from
the disease.

The Cerebellum

There are two approaches you can use to catch a fly ball. You could solve the
complex simultaneous differential equations controlling the ball’s movement as
well as further equations governing your own particular angle in viewing the
ball, and then compute even more equations on how to move your body, arm,
and hand to be in the right place at the right time.

This is not the approach that your brain adopts. It basically simplifies the
problem by collapsing a lot of equations into a simple trend model, considering
the trends of where the ball appears to be in your field of vision and how quickly
it is moving within it. It does the same thing with your hand, making essentially
linear predictions of the ball’s apparent position in your field of view and that of
your hand. The goal, of course, is to make sure they meet at the same point in
space and time. If the ball appears to be dropping too quickly and your hand
appears to be moving too slowly, your brain will direct your hand to move more
quickly, so that the trends will coincide. This “Gordian knot” solution to what
would otherwise be an intractable mathematical problem is called basis
functions, and they are carried out by the cerebellum, a bean-shaped and
appropriately baseball-sized region that sits on the brain stem.-

The cerebellum is an old-brain region that once controlled virtually all
hominid movements. It still contains half of the neurons in the brain, although
most are relatively small ones, so the region constitutes only about 10 percent of
the weight of the brain. The cerebellum likewise represents another instance of
massive repetition in the design of the brain. There is relatively little information
about its design in the genome, as its structure is a pattern of several neurons that
is repeated billions of times. As with the neocortex, there is uniformity across its
structure. 2

Most of the function of controlling our muscles has been taken over by the
neocortex, using the same pattern recognition algorithms that it uses for
perception and cognition. In the case of movement, we can more appropriately
refer to the neocortex’s function as pattern implementation. The neocortex does
make use of the memory in the cerebellum to record delicate scripts of
movements—for example, your signature and certain flourishes in artistic
expression such as music and dance. Studies of the role of the cerebellum during
the learning of handwriting by children reveal that the Purkinje cells of the
cerebellum actually sample the sequence of movements, with each one sensitive

to a specific sample.— Because most of our movement is now controlled by the
neocortex, many people can manage with a relatively modest obvious disability
even with significant damage to the cerebellum, except that their movements
may become less graceful.

The neocortex can also call upon the cerebellum to use its ability to
compute real-time basis functions to anticipate what the results of actions would
be that we are considering but have not yet carried out (and may never carry
out), as well as the actions or possible actions of others. It is another example of
the innate built-in linear predictors in the brain.

Substantial progress has been made in simulating the cerebellum with
respect to the ability to respond dynamically to sensory cues using the basis
functions I discussed above, in both bottom-up simulations (based on
biochemical models) and top-down simulations (based on mathematical models
of how each repeating unit in the cerebellum operates).—

Pleasure and Fear

Fear is the main source of superstition, and one of the main sources of
cruelty. To conquer fear is the beginning of wisdom.

—Bertrand Russell

Feel the fear and do it anyway.

—Susan Jeffers

If the neocortex is good at solving problems, then what is the main problem we
are trying to solve? The problem that evolution has always tried to solve is
survival of the species. That translates into the survival of the individual, and
each of us uses his or her own neocortex to interpret that in myriad ways. In
order to survive, animals need to procure their next meal while at the same time
avoiding becoming someone else’s meal. They also need to reproduce. The
earliest brains evolved pleasure and fear systems that rewarded the fulfillment of
these fundamental needs along with basic behaviors that facilitated them. As
environments and competing species gradually changed, biological evolution
made corresponding alterations. With the advent of hierarchical thinking, the
satisfaction of critical drives became more complex, as it was now subject to the
vast complex of ideas within ideas. But despite its considerable modulation by
the neocortex, the old brain is still alive and well and still motivating us with
pleasure and fear.

One region that is associated with pleasure is the nucleus accumbens. In
famous experiments conducted in the 1950s, rats that were able to directly
stimulate this small region (by pushing a lever that activated implanted
electrodes) preferred doing so to anything else, including having sex or eating,
ultimately exhausting and starving themselves to death.— In humans, other
regions are also involved in pleasure, such as the ventral pallidum and, of course,
the neocortex itself.

Pleasure is also regulated by chemicals such as dopamine and serotonin. It
is beyond the scope of this book to discuss these systems in detail, but it is
important to recognize that we have inherited these mechanisms from our
premammalian cousins. It is the job of our neocortex to enable us to be the

master of pleasure and fear and not their slave. To the extent that we are often
subject to addictive behaviors, the neocortex is not always successful in this
endeavor. Dopamine in particular is a neurotransmitter involved in the
experience of pleasure. If anything good happens to us—winning the lottery,
gaining the recognition of our peers, getting a hug from a loved one, or even
subtle achievements such as getting a friend to laugh at a joke—we experience a
release of dopamine. Sometimes we, like the rats who died overstimulating their
nucleus accumbens, use a shortcut to achieve these bursts of pleasure, which is
not always a good idea.

Gambling, for example, can release dopamine, at least when you win, but
this is dependent on its inherent lack of predictability. Gambling may work for
the purpose of releasing dopamine for a while, but given that the odds are
intentionally stacked against you (otherwise the business model of a casino
wouldn’t work), it can become ruinous as a regular strategy. Similar dangers are
associated with any addictive behavior. A particular genetic mutation of the
dopamine-receptor D2 gene causes especially strong feelings of pleasure from
initial experiences with addictive substances and behaviors, but as is well known
(but not always well heeded), the ability of these substances to produce pleasure
on subsequent use gradually declines. Another genetic mutation results in
people’s not receiving normal levels of dopamine release from everyday
accomplishments, which can also lead to seeking enhanced early experiences
with addictive activities. The minority of the population that has these genetic
proclivities to addiction creates an enormous social and medical problem. Even
those who manage to avoid severely addictive behaviors struggle with balancing
the rewards of dopamine release with the consequences of the behaviors that
release them.

Serotonin is a neurotransmitter that plays a major role in the regulation of
mood. In higher levels it is associated with feelings of well-being and
contentment. Serotonin has other functions, including modulating synaptic
strength, appetite, sleep, sexual desire, and digestion. Antidepression drugs such
as selective serotonin reuptake inhibitors (which tend to increase serotonin levels
available to receptors) tend to have far-reaching effects, not all of them desirable
(such as suppressing libido). Unlike actions in the neocortex, where recognition
of patterns and activations of axons affect only a small number of neocortical
circuits at a time, these substances affect large regions of the brain or even the
entire nervous system.

Each hemisphere of the human brain has an amygdala, which consists of an
almond-shaped region comprising several small lobes. The amygdala is also part
of the old brain and is involved in processing a number of types of emotional

responses, the most notable of which is fear. In premammalian animals, certain
preprogrammed stimuli representing danger feed directly into the amygdala,
which in turn triggers the “fight or flight” mechanism. In humans the amygdala
now depends on perceptions of danger to be transmitted by the neocortex. A
negative comment by your boss, for example, might trigger such a response by
generating the fear of losing your job (or maybe not, if you have confidence in a
plan B). Once the amygdala does decide that danger is ahead, an ancient
sequence of events occurs. The amygdala signals the pituitary gland to release a
hormone called ACTH (adrenocorticotropin). This in turn triggers the stress
hormone cortisol from the adrenal glands, which results in more energy being
provided to your muscles and nervous system. The adrenal glands also produce
adrenaline and noradrenaline, which suppress your digestive, immune, and
reproductive systems (figuring that these are not high-priority processes in an
emergency). Levels of blood pressure, blood sugar, cholesterol, and fibrinogen
(which speeds blood clotting) all rise. Heart rate and respiration go up. Even
your pupils dilate so that you have better visual acuity of your enemy or your
escape route. This is all very useful if a real danger such as a predator suddenly
crosses your path. It is well known that in today’s world, the chronic activation
of this fight-or-flight mechanism can lead to permanent health damage in terms
of hypertension, high cholesterol levels, and other problems.

The system of global neurotransmitter levels, such as serotonin, and
hormone levels, such as dopamine, is intricate, and we could spend the rest of
this book on the issue (as a great many books have done), but it is worth pointing
out that the bandwidth of information (the rate of information processing) in this
system is very low compared with the bandwidth of the neocortex. There are
only a limited number of substances involved and the levels of these chemicals
tend to change slowly and are relatively universal across the brain, as compared
with the neocortex, which is composed of hundreds of trillions of connections
that can change quickly.

It is fair to say that our emotional experiences take place in both the old and
the new brains. Thinking takes place in the new brain (the neocortex), but
feeling takes place in both. Any emulation of human behavior will therefore
need to model both. However, if it is just human cognitive intelligence that we
are after, the neocortex is sufficient. We can replace the old brain with the more
direct motivation of a nonbiological neocortex to achieve the goals that we
assign to it. For example, in the case of Watson, the goal was simply stated:
Come up with correct answers to Jeopardy! queries (albeit these were further
modulated by a program that understood Jeopardy! wagering). In the case of the
new Watson system being jointly developed by Nuance and IBM for medical

knowledge, the goal is to help treat human disease. Future systems can have
goals such as actually curing disease and alleviating poverty. A lot of the
pleasure-fear struggle is already obsolete for humans, as the old brain evolved
long before even primitive human society got started; indeed most of it is
reptilian.

There is a continual struggle in the human brain as to whether the old or the
new brain is in charge. The old brain tries to set the agenda with its control of
pleasure and fear experiences, whereas the new brain is continually trying to
understand the relatively primitive algorithms of the old brain and seeking to
manipulate it to its own agenda. Keep in mind that the amygdala is unable to
evaluate danger on its own—in the human brain it relies on the neocortex to
make those judgments. Is that person a friend or a foe, a lover or a threat? Only
the neocortex can decide.

To the extent that we are not directly engaged in mortal combat and hunting
for food, we have succeeded in at least partially sublimating our ancient drives to
more creative endeavors. On that note, we’ll discuss creativity and love in the
next chapter .

CHAPTER 6

TRANSCENDENT ABILITIES

This is my simple religion. There is no need for temples; no need for
complicated philosophy. Our own brain, our own heart is our temple; the
philosophy is kindness.

—The Dalai Lama

My hand moves because certain forces—electric, magnetic, or whatever
“nerve-force” may prove to be—are impressed on it by my brain. This
nerve-force, stored in the brain, would probably be traceable, if Science
were complete, to chemical forces supplied to the brain by the blood, and
ultimately derived from the food I eat and the air I breathe.

—Lewis Carroll

Our emotional thoughts also take place in the neocortex but are influenced by
portions of the brain ranging from ancient brain regions such as the amygdala to
some evolutionarily recent brain structures such as the spindle neurons, which
appear to play a key role in higher-level emotions. Unlike the regular and logical
recursive structures found in the cerebral cortex, the spindle neurons have highly
irregular shapes and connections. They are the largest neurons in the human
brain, spanning its entire breadth. They are deeply interconnected, with hundreds
of thousands of connections tying together diverse portions of the neocortex.

As mentioned earlier, the insula helps process sensory signals, but it also
plays a key role in higher-level emotions. It is this region from which the spindle
cells originate. Functional magnetic resonance imaging (fMRI) scans have
revealed that these cells are particularly active when a person is dealing with
emotions such as love, anger, sadness, and sexual desire. Situations that strongly
activate them include when a subject looks at her partner or hears her child
crying.

Spindle cells have long neural filaments called apical dendrites, which are

able to connect to faraway neocortical regions. Such “deep” interconnectedness,
in which certain neurons provide connections across numerous regions, is a
feature that occurs increasingly as we go up the evolutionary ladder. It is not
surprising that the spindle cells, involved as they are in handling emotion and
moral judgment, would have this form of connectedness, given the ability of
higher-level emotional reactions to touch on diverse topics and thoughts.
Because of their links to many other parts of the brain, the high-level emotions
that spindle cells process are affected by all of our perceptual and cognitive
regions. It is important to point out that these cells are not doing rational
problem solving, which is why we don’t have rational control over our responses
to music or over falling in love. The rest of the brain is heavily engaged,
however, in trying to make sense of our mysterious high-level emotions.

There are relatively few spindle cells: only about 80,000, with
approximately 45,000 in the right hemisphere and 35,000 in the left. This
disparity is at least one reason for the perception that emotional intelligence is
the province of the right brain, although the disproportion is modest. Gorillas
have about 16,000 of these cells, bonobos about 2,100, and chimpanzees about
1,800. Other mammals lack them completely.

Anthropologists believe that spindle cells made their first appearance 10 to
15 million years ago in the as yet undiscovered common ancestor to apes and
hominids (precursors to humans) and rapidly increased in numbers around
100,000 years ago. Interestingly, spindle cells do not exist in newborn humans
but begin to appear only at around the age of four months and increase
significantly in number from ages one to three. Children’s ability to deal with
moral issues and perceive such higher-level emotions as love develop during this
same period.

Aptitude

Wolfgang Amadeus Mozart (1756-1791) wrote a minuet when he was five. At
age six he performed for the empress Maria Theresa at the imperial court in
Vienna. He went on to compose six hundred pieces, including forty-one
symphonies, before his death at age thirty-five, and is widely regarded as the
greatest composer in the European classical tradition. One might say that he had
an aptitude for music.

So what does this mean in the context of the pattern recognition theory of
mind? Clearly part of what we regard as aptitude is the product of nurture, that is
to say, the influences of environment and other people. Mozart was born into a
musical family. His father, Leopold, was a composer and kapellmeister (literally
musical leader) of the court orchestra of the archbishop of Salzburg. The young
Mozart was immersed in music, and his father started teaching him the violin
and clavier (a keyboard instrument) at the age of three.

However, environmental influences alone do not fully explain Mozart’s
genius. There is clearly a nature component as well. What form does this take?
As I wrote in chapter 4 . different regions of the neocortex have become
optimized (by biological evolution) for certain types of patterns. Even though the
basic pattern recognition algorithm of the modules is uniform across the
neocortex, since certain types of patterns tend to flow through particular regions
(faces through the fusiform gyrus, for example), those regions will become
better at processing the associated patterns. However, there are numerous
parameters that govern how the algorithm is actually carried out in each module.
For example, how close a match is required for a pattern to be recognized? How
is that threshold modified if a higher-level module sends a signal that its pattern
is “expected”? How are the size parameters considered? These and other factors
have been set differently in different regions to be advantageous for particular
types of patterns. In our work with similar methods in artificial intelligence, we
have noticed the same phenomenon and have used simulations of evolution to
optimize these parameters.

If particular regions can be optimized for different types of patterns, then it
follows that individual brains will also vary in their ability to learn, recognize,
and create certain types of patterns. For example, a brain can have an innate
aptitude for music by being better able to recognize rhythmic patterns, or to
better understand the geometric arrangements of harmonies. The phenomenon of

perfect pitch (the ability to recognize and to reproduce a pitch without an
external reference), which is correlated with musical talent, appears to have a
genetic basis, although the ability needs to be developed, so it is likely to be a
combination of nature and nurture. The genetic basis of perfect pitch is likely to
reside outside the neocortex in the preprocessing of auditory information,
whereas the learned aspect resides in the neocortex.

There are other skills that contribute to degrees of competency, whether of
the routine variety or of the legendary genius. Neocortical abilities—for
example, the ability of the neocortex to master the signals of fear that the
amygdala generates (when presented with disapproval)—play a significant role,
as do attributes such as confidence, organizational skills, and the ability to
influence others. A very important skill I noted earlier is the courage to pursue
ideas that go against the grain of orthodoxy. Invariably, people we regard as
geniuses pursued their own mental experiments in ways that were not initially
understood or appreciated by their peers. Although Mozart did gain recognition
in his lifetime, most of the adulation came later. He died a pauper, buried in a
common grave, and only two other musicians showed up at his funeral.

Creativity

Creativity is a drug I cannot live without.

—Cecil B. DeMille

The problem is never how to get new, innovative thoughts into your mind,
but how to get old ones out. Every mind is a building filled with archaic
furniture. Clean out a corner of your mind and creativity will instantly fill
it.

—Dee Hock

Humanity can be quite cold to those whose eyes see the world differently.

—Eric A. Burns

Creativity can solve almost any problem. The creative act, the defeat of
habit by originality, overcomes everything.

—George Lois

A key aspect of creativity is the process of finding great metaphors—symbols
that represent something else. The neocortex is a great metaphor machine, which
accounts for why we are a uniquely creative species. Every one of the
approximately 300 million pattern recognizers in our neocortex is recognizing
and defining a pattern and giving it a name, which in the case of the neocortical
pattern recognition modules is simply the axon emerging from the pattern
recognizer that will fire when that pattern is found. That symbol in turn then
becomes part of another pattern. Each one of these patterns is essentially a
metaphor. The recognizers can fire up to 100 times a second, so we have the
potential of recognizing up to 30 billion metaphors a second. Of course not every
module is firing in every cycle—but it is fair to say that we are indeed
recognizing millions of metaphors a second.

Of course, some metaphors are more significant than others. Darwin
perceived that Charles Lyell’s insight on how very gradual changes from a

trickle of water could carve out great canyons was a powerful metaphor for how
a trickle of small evolutionary changes over thousands of generations could
carve out great changes in the differentiation of species. Thought experiments,
such as the one that Einstein used to illuminate the true meaning of the
Michelson-Morley experiment, are all metaphors, in the sense of being a “thing
regarded as representative or symbolic of something else,” to quote a dictionary
definition.

Do you see any metaphors in Sonnet 73 by Shakespeare?

That time of year thou mayst in me behold
When yellow leaves, or none, or few, do hang
Upon those boughs which shake against the cold,

Bare ruined choirs, where late the sweet birds sang.

In me thou seest the twilight of such day
As after sunset fadeth in the west,

Which by and by black night doth take away,

Death’s second self that seals up all in rest.

In me thou seest the glowing of such fire
That on the ashes of his youth doth lie,

As the deathbed whereon it must expire
Consumed with that which it was nourished by.

This thou perceiv’st, which makes thy love more strong,

To love that well which thou must leave ere long.

In this sonnet, the poet uses extensive metaphors to describe his advancing
age. His age is like late autumn, “when yellow leaves, or none, or few, do hang.”
The weather is cold and the birds can no longer sit on the branches, which he
calls “bare ruin’d choirs.” His age is like the twilight as the “sunset fadeth in the
west, which by and by black night doth take away.” He is the remains of a fire
“that on the ashes of his youth doth lie.” Indeed, all language is ultimately
metaphor, though some expressions of it are more memorable than others.

Finding a metaphor is the process of recognizing a pattern despite
differences in detail and context—an activity we undertake trivially every
moment of our lives. The metaphorical leaps that we consider of significance
tend to take place in the interstices of different disciplines. Working against this
essential force of creativity, however, is the pervasive trend toward ever greater
specialization in the sciences (and just about every other field as well). As
American mathematician Norbert Wiener (1894-1964) wrote in his seminal
book Cybernetics, published the year I was born (1948):

There are fields of scientific work, as we shall see in the body of this
book, which have been explored from the different sides of pure
mathematics, statistics, electrical engineering, and neurophysiology; in
which every single notion receives a separate name from each group, and in
which important work has been triplicated or quadruplicated, while still
other important work is delayed by the unavailability in one field of results
that may have already become classical in the next field.

It is these boundary regions which offer the richest opportunities to the
qualified investigator. They are at the same time the most refractory to the
accepted techniques of mass attack and the division of labor.

A technique I have used in my own work to combat increasing
specialization is to assemble the experts that I have gathered for a project (for
example, my speech recognition work included speech scientists, linguists,
psychoacousticians, and pattern recognition experts, not to mention computer
scientists) and encourage each one to teach the group his particular techniques
and terminology. We then throw out all of that terminology and make up our
own. Invariably we find metaphors from one field that solve problems in
another.

A mouse that finds an escape route when confronted with the household cat
—and can do so even if the situation is somewhat different from what it has ever
encountered before—is being creative. Our own creativity is orders of
magnitude greater than that of the mouse—and involves far more levels of
abstraction—because we have a much larger neocortex, which is capable of
greater levels of hierarchy. So one way to achieve greater creativity is by
effectively assembling more neocortex.

One approach to expand the available neocortex is through the
collaboration of multiple humans. This is accomplished routinely via the
communication between people gathered in a problem-solving community.
Recently there have been efforts to use online collaboration tools to harness the
power of real-time collaboration, which have shown success in mathematics and
other fields.-

The next step, of course, will be to expand the neocortex itself with its
nonbiological equivalent. This will be our ultimate act of creativity: to create the
capability of being creative. A nonbiological neocortex will ultimately be faster
and could rapidly search for the kinds of metaphors that inspired Darwin and
Einstein. It could systematically explore all of the overlapping boundaries
between our exponentially expanding frontiers of knowledge.

Some people express concern about what will happen to those who would

opt out of such mind expansion. I would point out that this additional
intelligence will essentially reside in the cloud (the exponentially expanding
network of computers that we connect to through online communication), where
most of our machine intelligence is now stored. When you use a search engine,
recognize speech from your phone, consult a virtual assistant such as Siri, or use
your phone to translate a sign into another language, the intelligence is not in the
device itself but in the cloud. Our expanded neocortex will be housed there too.
Whether we access such expanded intelligence through direct neural connection
or the way we do now—by interacting with it via our devices—is an arbitrary
distinction. In my view we will all become more creative through this pervasive
enhancement, whether we choose to opt in or out of direct connection to
humanity’s expanded intelligence. We have already outsourced much of our
personal, social, historical, and cultural memory to the cloud, and we will
ultimately do the same thing with our hierarchical thinking.

Einstein’s breakthrough resulted not only from his application of metaphors
through mind experiments but also from his courage in believing in the power of
those metaphors. He was willing to relinquish the traditional explanations that
failed to satisfy his experiments, and he was willing to withstand the ridicule of
his peers to the bizarre explanations that his metaphors implied. These qualities
—belief in metaphor and courage of conviction—are ones that we should be able
to program into our nonbiological neocortex as well.

Love

Clarity of mind means clarity of passion, too; this is why a great and clear
mind loves ardently and sees distinctly what it loves.

—Blaise Pascal

There is always some madness in love. But there is also always some
reason in madness.

—Friedrich Nietzsche

When you have seen as much of life as I have, you will not underestimate
the power of obsessive love.

—Albus Dumbledore, in J. K. Rowling, Harry Potter and the Half-
Blood Prince

I always like a good math solution to any love problem.

—Michael Patrick King, from the “Take Me Out to the Ballgame”
episode of Sex and the City

If you haven’t actually experienced ecstatic love personally, you have
undoubtedly heard about it. It is fair to say that a substantial fraction if not a
majority of the world’s art—stories, novels, music, dance, paintings, television
shows, and movies—is inspired by the stories of love in its earliest stages.

Science has recently gotten into the act as well, and we are now able to
identify the biochemical changes that occur when someone falls in love.
Dopamine is released, producing feelings of happiness and delight.
Norepinephrine levels soar, which lead to a racing heart and overall feelings of
exhilaration. These chemicals, along with phenylethylamine, produce elation,
high energy levels, focused attention, loss of appetite, and a general craving for
the object of one’s desire. Interestingly, recent research at University College in
London also shows that serotonin levels go down, similar to what happens in
obsessive-compulsive disorder, which is consistent with the obsessive nature of

early love.^ The high levels of dopamine and norepinephrine account for the
heightened short-term attention, euphoria, and craving of early love.

If these biochemical phenomena sound similar to those of the fight-or-flight
syndrome, they are, except that here we are running toward something or
someone; indeed, a cynic might say toward rather than away from danger. The
changes are also fully consistent with those of the early phases of addictive
behavior. The Roxy Music song “Love Is the Drug” is quite accurate in
describing this state (albeit the subject of the song is looking to score his next fix
of love). Studies of ecstatic religious experiences also show the same physical
phenomena; it can be said that the person having such an experience is falling in
love with God or whatever spiritual connection on which they are focused.

In the case of early romantic love, estrogen and testosterone certainly play a
role in establishing sex drive, but if sexual reproduction were the only
evolutionary objective of love, then the romantic aspect of the process would not
be necessary. As psychologist John William Money (1921-2006) wrote, “Lust is
lewd, love is lyrical.”

The ecstatic phase of love leads to the attachment phase and ultimately to a
long-term bond. There are chemicals that encourage this process as well,
including oxytocin and vasopressin. Consider two related species of voles: the
prairie vole and the montane vole. They are pretty much identical, except that
the prairie vole has receptors for oxytocin and vasopressin, whereas the montane
vole does not. The prairie vole is noted for lifetime monogamous relationships,
while the montane vole resorts almost exclusively to one-night stands. In the
case of voles, the oxytocin and vasopressin receptors are pretty much
determinative as to the nature of their love life.

While these chemicals are influential on humans as well, our neocortex has
taken a commanding role, as in everything else we do. Voles do have a
neocortex, but it is postage-stamp sized and flat and just large enough for them
to find a mate for life (or, in the case of montane voles, at least for the night) and
carry out other basic vole behaviors. We humans have sufficient additional
neocortex to engage in the expansive “lyrical” expressions to which Money
refers.

From an evolutionary perspective, love itself exists to meet the needs of the
neocortex. If we didn’t have a neocortex, then lust would be quite sufficient to
guarantee reproduction. The ecstatic instigation of love leads to attachment and
mature love, and results in a lasting bond. This in turn is designed to provide at
least the possibility of a stable environment for children while their own
neocortices undergo the critical learning needed to become responsible and
capable adults. Learning in a rich environment is inherently part of the method

of the neocortex. Indeed the same oxytocin and vasopressin hormone
mechanisms play a key role in establishing the critical bonding of parent
(especially mother) and child.

At the far end of the story of love, a loved one becomes a major part of our
neocortex. After decades of being together, a virtual other exists in the neocortex
such that we can anticipate every step of what our lover will say and do. Our
neocortical patterns are filled with the thoughts and patterns that reflect who they
are. When we lose that person, we literally lose part of ourselves. This is not just
a metaphor—all of the vast pattern recognizers that are filled with the patterns
reflecting the person we love suddenly change their nature. Although they can be
considered a precious way to keep that person alive within ourselves, the vast
neocortical patterns of a lost loved one turn suddenly from triggers of delight to
triggers of mourning.

The evolutionary basis for love and its phases is not the full story in today’s
world. We have already largely succeeded in liberating sex from its biological
function, in that we can have babies without sex and we can certainly have sex
without babies. The vast majority of sex takes place for its sensual and relational
purposes. And we routinely fall in love for purposes other than raising children.

Similarly, the vast expanse of artistic expression of all kinds that celebrates
love and its myriad forms dating back to antiquity is also an end in itself. Our
ability to create these enduring forms of transcendent knowledge—about love or
anything else—is precisely what makes our species unique.

The neocortex is biology’s greatest creation. In turn, it is the poems about
love—and all of our other creations—that represent the greatest inventions of
our neocortex.

CHAPTER 7

THE BIOLOGICALLY
INSPIRED DIGITAL
NEOCORTEX

Never trust anything that can think for itself if you can’t see where it keeps
its brain.

—Arthur Weasley, in J. K. Rowling, Harry Potter and the Prisoner
of Azkaban

No, I’m not interested in developing a powerful brain. All I’m after is just a
mediocre brain, something like the President of the American Telephone
and Telegraph Company.

—Alan Turing

A computer would deserve to be called intelligent if it could deceive a
human into believing that it was human.

—Alan Turing

I believe that at the end of the century the use of words and general
educated opinion will have altered so much that one will be able to speak of
machines thinking without expecting to be contradicted.

—Alan Turing

A mother rat will build a nest for her young even if she has never seen another

rat in her lifetime.- Similarly, a spider will spin a web, a caterpillar will create
her own cocoon, and a beaver will build a dam, even if no contemporary ever

showed them how to accomplish these complex tasks. That is not to say that
these are not learned behaviors. It is just that these animals did not learn them in
a single lifetime—they learned them over thousands of lifetimes. The evolution
of animal behavior does constitute a learning process, but it is learning by the
species, not by the individual, and the fruits of this learning process are encoded
in DNA.

To appreciate the significance of the evolution of the neocortex, consider
that it greatly sped up the process of learning (hierarchical knowledge) from
thousands of years to months (or less). Even if millions of animals in a particular
mammalian species failed to solve a problem (requiring a hierarchy of steps), it
required only one to accidentally stumble upon a solution. That new method
would then be copied and spread exponentially through the population.

We are now in a position to speed up the learning process by a factor of
thousands or millions once again by migrating from biological to nonbiological
intelligence. Once a digital neocortex learns a skill, it can transfer that know¬
how in minutes or even seconds. As one of many examples, at my first company,
Kurzweil Computer Products (now Nuance Speech Technologies), which I
founded in 1973, we spent years training a set of research computers to
recognize printed letters from scanned documents, a technology called omni-font
(any type font) optical character recognition (OCR). This particular technology
has now been in continual development for almost forty years, with the current
product called OmniPage from Nuance. If you want your computer to recognize
printed letters, you don’t need to spend years training it to do so, as we did—you
can simply download the evolved patterns already learned by the research
computers in the form of software. In the 1980s we began on speech recognition,
and that technology, which has also been in continuous development now for
several decades, is part of Siri. Again, you can download in seconds the evolved
patterns learned by the research computers over many years.

Ultimately we will create an artificial neocortex that has the full range and
flexibility of its human counterpart. Consider the benefits. Electronic circuits are
millions of times faster than our biological circuits. At first we will have to
devote all of this speed increase to compensating for the relative lack of
parallelism in our computers, but ultimately the digital neocortex will be much
faster than the biological variety and will only continue to increase in speed.

When we augment our own neocortex with a synthetic version, we won’t
have to worry about how much additional neocortex can physically fit into our
bodies and brains, as most of it will be in the cloud, like most of the computing
we use today. I estimated earlier that we have on the order of 300 million pattern
recognizers in our biological neocortex. That’s as much as could be squeezed

into our skulls even with the evolutionary innovation of a large forehead and
with the neocortex taking about 80 percent of the available space. As soon as we
start thinking in the cloud, there will be no natural limits—we will be able to use
billions or trillions of pattern recognizers, basically whatever we need, and
whatever the law of accelerating returns can provide at each point in time.

In order for a digital neocortex to learn a new skill, it will still require many
iterations of education, just as a biological neocortex does, but once a single
digital neocortex somewhere and at some time learns something, it can share that
knowledge with every other digital neocortex without delay. We can each have
our own private neocortex extenders in the cloud, just as we have our own
private stores of personal data today.

Last but not least, we will be able to back up the digital portion of our
intelligence. As we have seen, it is not just a metaphor to state that there is
information contained in our neocortex, and it is frightening to contemplate that
none of this information is backed up today. There is, of course, one way in
which we do back up some of the information in our brains—by writing it down.
The ability to transfer at least some of our thinking to a medium that can outlast
our biological bodies was a huge step forward, but a great deal of data in our
brains continues to remain vulnerable.

Brain Simulations

One approach to building a digital brain is to simulate precisely a biological one.
For example, Harvard brain sciences doctoral student David Dalrymple (born in
1991) is planning to simulate the brain of a nematode (a roundworm).-
Dalrymple selected the nematode because of its relatively simple nervous
system, which consists of about 300 neurons, and which he plans to simulate at
the very detailed level of molecules. He will also create a computer simulation of
its body as well as its environment so that his virtual nematode can hunt for
(virtual) food and do the other things that nematodes are good at. Dalrymple says
it is likely to be the first complete brain upload from a biological animal to a
virtual one that lives in a virtual world. Like his simulated nematode, whether
even biological nematodes are conscious is open to debate, although in their
struggle to eat, digest food, avoid predators, and reproduce, they do have
experiences to be conscious of.

At the opposite end of the spectmm, Henry Markram’s Blue Brain Project
is planning to simulate the human brain, including the entire neocortex as well as
the old-brain regions such as the hippocampus, amygdala, and cerebellum. His
planned simulations will be built at varying degrees of detail, up to a full
simulation at the molecular level. As I reported in chapter 4 . Markram has
discovered a key module of several dozen neurons that is repeated over and over
again in the neocortex, demonstrating that learning is done by these modules and
not by individual neurons.

Markram’s progress has been scaling up at an exponential pace. He
simulated one neuron in 2005, the year the project was initiated. In 2008 his
team simulated an entire neocortical column of a rat brain, consisting of 10,000
neurons. By 2011 this expanded to 100 columns, totaling a million cells, which
he calls a mesocircuit. One controversy concerning Markram’s work is how to
verify that the simulations are accurate. In order to do this, these simulations will
need to demonstrate learning that I discuss below.

He projects simulating an entire rat brain of 100 mesocircuits, totaling 100
million neurons and about a trillion synapses, by 2014. In a talk at the 2009 TED
conference at Oxford, Markram said, “It is not impossible to build a human
brain, and we can do it in 10 years.”- His most recent target for a full brain
simulation is 2023.-

Markram and his team are basing their model on detailed anatomical and

electrochemical analyses of actual neurons. Using an automated device they
created called a patch-clamp robot, they are measuring the specific ion channels,
neurotransmitters, and enzymes that are responsible for the electrochemical
activity within each neuron. Their automated system was able to do thirty years
of analysis in six months, according to Markram. It was from these analyses that
they noticed the “Lego memory” units that are the basic functional units of the
neocortex.

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Significant contributions to the technology of robotic patch-clamping was
made by MIT neuroscientist Ed Boyden, Georgia Tech mechanical engineering
professor Craig Forest, and Forest’s graduate student Suhasa Kodandaramaiah.
They demonstrated an automated system with one-micrometer precision that can
perform scanning of neural tissue at very close range without damaging the
delicate membranes of the neurons. “This is something a robot can do that a
human can’t,” Boyden commented.

To return to Markram’s simulation, after simulating one neocortical column,
Markram was quoted as saying, “Now we just have to scale it up.”- Scaling is
certainly one big factor, but there is one other key hurdle, which is learning. If
the Blue Brain Project brain is to “speak and have an intelligence and behave
very much as a human does,” which is how Markram described his goal in a

BBC interview in 2009, then it will need to have sufficient content in its
simulated neocortex to perform those tasks.- As anyone who has tried to hold a
conversation with a newborn can attest, there is a lot of learning that must be
achieved before this is feasible.

The tip of the patch-clamping robot developed at MIT and Georgia Tech
scanning neural tissue.

There are two obvious ways this can be done in a simulated brain such as
Blue Brain. One would be to have the brain learn this content the way a human
brain does. It can start out like a newborn human baby with an innate capacity
for acquiring hierarchical knowledge and with certain transformations
preprogrammed in its sensory preprocessing regions. But the learning that takes
place between a biological infant and a human person who can hold a
conversation would need to occur in a comparable manner in nonbiological
learning. The problem with that approach is that a brain that is being simulated
at the level of detail anticipated for Blue Brain is not expected to run in real time
until at least the early 2020s. Even running in real time would be too slow unless
the researchers are prepared to wait a decade or two to reach intellectual parity
with an adult human, although real-time performance will get steadily faster as
computers continue to grow in price/performance.

The other approach is to take one or more biological human brains that
have already gained sufficient knowledge to converse in meaningful language

and to otherwise behave in a mature manner and copy their neocortical patterns
into the simulated brain. The problem with this method is that it requires a
noninvasive and nondestructive scanning technology of sufficient spatial and
temporal resolution and speed to perform such a task quickly and completely. I
would not expect such an “uploading” technology to be available until around
the 2040s. (The computational requirement to simulate a brain at that degree of
precision, which I estimate to be 10 19 calculations per second, will be available
in a supercomputer according to my projections by the early 2020s; however, the
necessary nondestructive brain scanning technologies will take longer.)

There is a third approach, which is the one I believe simulation projects
such as Blue Brain will need to pursue. One can simplify molecular models by
creating functional equivalents at different levels of specificity, ranging from my
own functional algorithmic method (as described in this book) to simulations
that are closer to full molecular simulations. The speed of learning can thereby
be increased by a factor of hundreds or thousands depending on the degree of
simplification used. An educational program can be devised for the simulated
brain (using the functional model) that it can learn relatively quickly. Then the
full molecular simulation can be substituted for the simplified model while still
using its accumulated learning. We can then simulate learning with the full
molecular model at a much slower speed.

American computer scientist Dharmendra Modha and his IBM colleagues
have created a cell-by-cell simulation of a portion of the human visual neocortex
comprising 1.6 billion virtual neurons and 9 trillion synapses, which is
equivalent to a cat neocortex. It runs 100 times slower than real time on an IBM
BlueGene/P supercomputer consisting of 147,456 processors. The work received
the Gordon Bell Prize from the Association for Computing Machinery.

The purpose of a brain simulation project such as Blue Brain and Modha’s
neocortex simulations is specifically to refine and confirm a functional model.
AI at the human level will principally use the type of functional algorithmic
model discussed in this book. However, molecular simulations will help us to
perfect that model and to fully understand which details are important. In my
development of speech recognition technology in the 1980s and 1990s, we were
able to refine our algorithms once the actual transformations performed by the
auditory nerve and early portions of the auditory cortex were understood. Even if
our functional model was perfect, understanding exactly how it is actually
implemented in our biological brains will reveal important knowledge about
human function and dysfunction.

We will need detailed data on actual brains to create biologically based
simulations. MarkranTs team is collecting its own data. There are large-scale

projects to gather this type of data and make it generally available to scientists.
For example, Cold Spring Harbor Laboratory in New York has collected 500
terabytes of data by scanning a mammal brain (a mouse), which they made
available in June 2012. Their project allows a user to explore a brain similarly to
the way Google Earth allows one to explore the surface of the planet. You can
move around the entire brain and zoom in to see individual neurons and their
connections. You can highlight a single connection and then follow its path
through the brain.

Sixteen sections of the National Institutes of Health have gotten together
and sponsored a major initiative called the Human Connectome Project with
$38.5 million of funding.- Led by Washington University in St. Louis, the
University of Minnesota, Harvard University, Massachusetts General Hospital,
and the University of California at Los Angeles, the project seeks to create a
similar three-dimensional map of connections in the human brain. The project is
using a variety of noninvasive scanning technologies, including new forms of
MRI, magnetoencephalography (measuring the magnetic fields produced by the
electrical activity in the brain), and diffusion tractography (a method to trace the
pathways of fiber bundles in the brain). As I point out in chapter 10 . the spatial
resolution of noninvasive scanning of the brain is improving at an exponential
rate. The research by Van J. Wedeen and his colleagues at Massachusetts
General Hospital showing a highly regular gridlike structure of the wiring of the
neocortex that I described in chapter 4 is one early result from this project.

Oxford University computational neuroscientist Anders Sandberg (born in
1972) and Swedish philosopher Nick Bostrom (born in 1973) have written the
comprehensive Whole Brain Emulation: A Roadmap, which details the
requirements for simulating the human brain (and other types of brains) at
different levels of specificity from high-level functional models to simulating
molecules.- The report does not provide a timeline, but it does describe the
requirements to simulate different types of brains at varying levels of precision
in terms of brain scanning, modeling, storage, and computation. The report
projects ongoing exponential gains in all of these areas of capability and argues
that the requirements to simulate the human brain at a high level of detail are
coming into place.

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neuroscience

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Full c*ll
simulation

Body

simulation

Partial

emulatio>ns

Organism

simulation

Large

mammal

emulation

Small

mammal

emulation

Invertefccate

emulation

organism

emulation

Simulation

hardware

An outline of Whole Brain Emulation: A Roadmap by Anders Sandberg
and Nick Bostrom.

Neural Nets

In 1964, at the age of sixteen, I wrote to Frank Rosenblatt (1928-1971), a
professor at Cornell University, inquiring about a machine called the Mark 1
Perceptron. He had created it four years earlier, and it was described as having
brainlike properties. He invited me to visit him and try the machine out.

The Perceptron was built from what he claimed were electronic models of
neurons. Input consisted of values arranged in two dimensions. For speech, one
dimension represented frequency and the other time, so each value represented
the intensity of a frequency at a given point in time. For images, each point was
a pixel in a two-dimensional image. Each point of a given input was randomly
connected to the inputs of the first layer of simulated neurons. Every connection
had an associated synaptic strength, which represented its importance, and which
was initially set at a random value. Each neuron added up the signals coming
into it. If the combined signal exceeded a particular threshold, the neuron fired
and sent a signal to its output connection; if the combined input signal did not
exceed the threshold, the neuron did not fire, and its output was zero. The output
of each neuron was randomly connected to the inputs of the neurons in the next
layer. The Mark 1 Perceptron had three layers, which could be organized in a
variety of configurations. For example, one layer might feed back to an earlier
one. At the top layer, the output of one or more neurons, also randomly selected,
provided the answer. (For an algorithmic description of neural nets, see this
endnote.)-

Since the neural net wiring and synaptic weights are initially set randomly,
the answers of an untrained neural net are also random. The key to a neural net,
therefore, is that it must learn its subject matter, just like the mammalian brains
on which it’s supposedly modeled. A neural net starts out ignorant; its teacher—
which may be a human, a computer program, or perhaps another, more mature
neural net that has already learned its lessons—rewards the student neural net
when it generates the correct output and punishes it when it does not. This
feedback is in turn used by the student neural net to adjust the strength of each
interneuronal connection. Connections that are consistent with the correct
answer are made stronger. Those that advocate a wrong answer are weakened.

Over time the neural net organizes itself to provide the correct answers
without coaching. Experiments have shown that neural nets can learn their
subject matter even with unreliable teachers. If the teacher is correct only 60
percent of the time, the student neural net will still learn its lessons with an
accuracy approaching 100 percent.

However, limitations in the range of material that the Perceptron was
capable of learning quickly became apparent. When I visited Professor
Rosenblatt in 1964, I tried simple modifications to the input. The system was set
up to recognize printed letters, and would recognize them quite accurately. It did
a fairly good job of autoassociation (that is, it could recognize the letters even if
I covered parts of them), but fared less well with invariance (that is, generalizing
over size and font changes, which confused it).

During the last half of the 1960s, these neural nets became enormously
popular, and the field of “connectionism” took over at least half of the artificial
intelligence field. The more traditional approach to AI, meanwhile, included

direct attempts to program solutions to specific problems, such as how to
recognize the invariant properties of printed letters.

Another person I visited in 1964 was Marvin Minsky (born in 1927), one of
the founders of the artificial intelligence field. Despite having done some
pioneering work on neural nets himself in the 1950s, he was concerned with the
great surge of interest in this technique. Part of the allure of neural nets was that
they supposedly did not require programming—they would learn solutions to
problems on their own. In 1965 I entered MIT as a student with Professor
Minsky as my mentor, and I shared his skepticism about the craze for
“connectionism.”

In 1969 Minsky and Seymour Papert (born in 1928), the two cofounders of
the MIT Artificial Intelligence Laboratory, wrote a book called Perceptrons,
which presented a single core theorem: specifically, that a Perceptron was
inherently incapable of determining whether or not an image was connected. The
book created a firestorm. Determining whether or not an image is connected is a
task that humans can do very easily, and it is also a straightforward process to
program a computer to make this discrimination. The fact that Perceptrons could
not do so was considered by many to be a fatal flaw.

Two images from the cover of the book Perceptrons by Marvin Minsky
and Seymour Papert. The top image is not connected (that is, the dark area
consists of two disconnected parts). The bottom image is connected. A
human can readily determine this, as can a simple software program. A
feedforward Perceptron such as Frank Rosenblatt’s Mark 1 Perceptron
cannot make this determination.

Perceptrons, however, was widely interpreted to imply more than it actually
did. Minsky and Papert’s theorem applied only to a particular type of neural net
called a feedforward neural net (a category that does include Rosenblatt’s
Perceptron); other types of neural nets did not have this limitation. Still, the book
did manage to largely kill most funding for neural net research during the 1970s.
The field did return in the 1980s with attempts to use what were claimed to be
more realistic models of biological neurons and ones that avoided the limitations
implied by the Minsky-Papert Perceptron theorem. Nevertheless, the ability of
the neocortex to solve the invariance problem, a key to its strength, was a skill
that remained elusive for the resurgent connectionist field.

Sparse Coding: Vector Quantization

In the early 1980s I started a project devoted to another classical pattern
recognition problem: understanding human speech. At first, we used traditional
AI approaches by directly programming expert knowledge about the
fundamental units of speech—phonemes—and rules from linguists on how
people string phonemes together to form words and phrases. Each phoneme has
distinctive frequency patterns. For example, we knew that vowels such as “e”
and “ah” are characterized by certain resonant frequencies called formants, with
a characteristic ratio of formants for each phoneme. Sibilant sounds such as “z”
and “s” are characterized by a burst of noise that spans many frequencies.

We captured speech as a waveform, which we then converted into multiple
frequency bands (perceived as pitches) using a bank of frequency filters. The
result of this transformation could be visualized and was called a spectrogram
(see page 136 1.

The filter bank is copying what the human cochlea does, which is the initial
step in our biological processing of sound. The software first identified
phonemes based on distinguishing patterns of frequencies and then identified
words based on identifying characteristic sequences of phonemes.

A spectrogram of three vowels. From left to right: [i] as in “appreciate,”
[u] as in “acoustic,” and [a] as in “ah.” The Y axis represents frequency of
sound. The darker the band the more acoustic energy there is at that
frequency.

A spectrogram of a person saying the word “hide.” The horizontal lines
show the formants, which are sustained frequencies that have especially
high energy.—

The result was partially successful. We could train our device to learn the
patterns for a particular person using a moderate-sized vocabulary, measured in
thousands of words. When we attempted to recognize tens of thousands of
words, handle multiple speakers, and allow fully continuous speech (that is,
speech with no pauses between words), we ran into the invariance problem.
Different people enunciated the same phoneme differently—for example, one
person’s “e” phoneme may sound like someone else’s “ah.” Even the same
person was inconsistent in the way she spoke a particular phoneme. The pattern
of a phoneme was often affected by other phonemes nearby. Many phonemes
were left out completely. The pronunciation of words (that is, how phonemes are
strung together to form words) was also highly variable and dependent on
context. The linguistic mles we had programmed were breaking down and could
not keep up with the extreme variability of spoken language.

It became clear to me at the time that the essence of human pattern and
conceptual recognition was based on hierarchies. This is certainly apparent for
human language, which constitutes an elaborate hierarchy of structures. But
what is the element at the base of the structures? That was the first question I
considered as I looked for ways to automatically recognize fully normal human
speech.

Sound enters the ear as a vibration of the air and is converted by the
approximately 3,000 inner hair cells in the cochlea into multiple frequency
bands. Each hair cell is tuned to a particular frequency (note that we perceive
frequencies as tones) and each acts as a frequency filter, emitting a signal
whenever there is sound at or near its resonant frequency. As it leaves the human
cochlea, sound is thereby represented by approximately 3,000 separate signals,
each one signifying the time-varying intensity of a narrow band of frequencies

(with substantial overlap among these bands).

Even though it was apparent that the brain was massively parallel, it
seemed impossible to me that it was doing pattern matching on 3,000 separate
auditory signals. I doubted that evolution could have been that inefficient. We
now know that very substantial data reduction does indeed take place in the
auditory nerve before sound signals ever reach the neocortex.

In our software-based speech recognizers, we also used filters implemented
as software—sixteen to be exact (which we later increased to thirty-two, as we
found there was not much benefit to going much higher than this). So in our
system, each point in time was represented by sixteen numbers. We needed to
reduce these sixteen streams of data into one while at the same emphasizing the
features that are significant in recognizing speech.

We used a mathematically optimal technique to accomplish this, called
vector quantization. Consider that at any particular point in time, sound (at least
from one ear) was represented by our software by sixteen different numbers: that
is, the output of the sixteen frequency filters. (In the human auditory system the
figure would be 3,000, representing the output of the 3,000 cochlea inner hair
cells.) In mathematical terminology, each such set of numbers (whether 3,000 in
the biological case or 16 in our software implementation) is called a vector.

For simplicity, let’s consider the process of vector quantization with vectors
of two numbers. Each vector can be considered a point in two-dimensional
space.

Y

One vector consisting
of 2 numbers

X

If we have a very large sample of such vectors and plot them, we are likely
to notice clusters forming.

Y

• •

Multiple two-dimensional
vectors forming 3 clusters

• • •

• ••
• • •
• •

X

In order to identify the clusters, we need to decide how many we will allow.
In our project we generally allowed 1,024 clusters so that we could number them
and assign each cluster a 10-bit label (because 2 10 = 1,024). Our sample of
vectors represents the diversity that we expect. We tentatively assign the first
1,024 vectors to be one-point clusters. We then consider the 1,025th vector and
find the point that it is closest to. If that distance is greater than the smallest
distance between any pair of the 1,024 points, we consider it as the beginning of
a new cluster. We then collapse the two (one-point) clusters that are closest
together into a single cluster. We are thus still left with 1,024 clusters. After
processing the 1,025th vector, one of those clusters now has more than one point.
We keep processing points in this way, always maintaining 1,024 clusters. After
we have processed all the points, we represent each multipoint cluster by the
geometric center of the points in that cluster.

• • •

A cluster of points. We
represent it using a

single point that Is the
geometric center of all
the points in the cluster.

We continue this iterative process until we have run through all the sample
points. Typically we would process millions of points into 1,024 (2 10 ) clusters;
we’ve also used 2,048 (2 11 ) or 4,096 (2 12 ) clusters. Each cluster is represented
by one vector that is at the geometric center of all the points in that cluster. Thus
the total of the distances of all the points in the cluster to the center point of the
cluster is as small as possible.

The result of this technique is that instead of having the millions of points
that we started with (and an even larger number of possible points), we have now
reduced the data to just 1,024 points that use the space of possibilities optimally.
Parts of the space that are never used are not assigned any clusters.

We then assign a number to each cluster (in our case, 0 to 1,023). That
number is the reduced, “quantized” representation of that cluster, which is why

the technique is called vector quantization. Any new input vector that arrives in
the future is then represented by the number of the cluster whose center point is
closest to this new input vector.

We can now precompute a table with the distance of the center point of
every cluster to every other center point. We thereby have instantly available the
distance of this new input vector (which we represent by this quantized point—
in other words, by the number of the cluster that this new point is closest to) to
every other cluster. Since we are only representing points by their closest cluster,
we now know the distance of this point to any other possible point that might
come along.

I described the technique above using vectors with only two numbers each,
but working with sixteen-element vectors is entirely analogous to the simpler
example. Because we chose vectors with sixteen numbers representing sixteen
different frequency bands, each point in our system was a point in sixteen¬
dimensional space. It is difficult for us to imagine a space with more than three
dimensions (perhaps four, if we include time), but mathematics has no such
inhibitions.

We have accomplished four things with this process. First, we have greatly
reduced the complexity of the data. Second, we have reduced sixteen¬
dimensional data to one-dimensional data (that is, each sample is now a single
number). Third, we have improved our ability to find invariant features, because
we are emphasizing portions of the space of possible sounds that convey the
most information. Most combinations of frequencies are physically impossible
or at least very unlikely, so there is no reason to give equal space to unlikely
combinations of inputs as to likely ones. This technique reduces the data to
equally likely possibilities. The fourth benefit is that we can use one-dimensional
pattern recognizers, even though the original data consisted of many more
dimensions. This turned out to be the most efficient approach to utilizing
available computational resources.

Reading Your Mind with Hidden Markov Models

With vector quantization, we simplified the data in a way that emphasized key
features, but we still needed a way to represent the hierarchy of invariant
features that would make sense of new information. Having worked in the field
of pattern recognition at that time (the early 1980s) for twenty years, I knew that
one-dimensional representations were far more powerful, efficient, and
amenable to invariant results. There was not a lot known about the neocortex in
the early 1980s, but based on my experience with a variety of pattern recognition
problems, I assumed that the brain was also likely to be reducing its
multidimensional data (whether from the eyes, the ears, or the skin) using a one¬
dimensional representation, especially as concepts rose in the neocortex’s
hierarchy.

For the speech recognition problem, the organization of information in the
speech signal appeared to be a hierarchy of patterns, with each pattern
represented by a linear string of elements with a forward direction. Each element
of a pattern could be another pattern at a lower level, or a fundamental unit of
input (which in the case of speech recognition would be our quantized vectors).

You will recognize this situation as consistent with the model of the
neocortex that I presented earlier. Human speech, therefore, is produced by a
hierarchy of linear patterns in the brain. If we could simply examine these
patterns in the brain of the person speaking, it would be a simple matter to match
her new speech utterances against her brain patterns and understand what the
person was saying. Unfortunately we do not have direct access to the brain of the
speaker—the only information we have is what she actually said. Of course, that
is the whole point of spoken language—the speaker is sharing a piece of her
mind with her utterance.

So I wondered: Was there a mathematical technique that would enable us to
infer the patterns in the speaker’s brain based on her spoken words? One
utterance would obviously not be sufficient, but if we had a large number of
samples, could we use that information to essentially read the patterns in the
speaker’s neocortex (or at least formulate something mathematically equivalent
that would enable us to recognize new utterances)?

People often fail to appreciate how powerful mathematics can be—keep in
mind that our ability to search much of human knowledge in a fraction of a
second with search engines is based on a mathematical technique. For the speech

recognition problem I was facing in the early 1980s, it turned out that the
technique of hidden Markov models fit the bill rather perfectly. The Russian
mathematician Andrei Andreyevich Markov (1856-1922) built a mathematical
theory of hierarchical sequences of states. The model was based on the
possibility of traversing the states in one chain, and if that was successful,
triggering a state in the next higher level in the hierarchy. Sound familiar?

Pi,i P2.2 P3.3 P 4,4

V ----w ^ ^

© P 12 P2 3 P3 4 P4 5 “*

' Pi .3 P 2 ,4 p 3 5 -

A simple example of one layer of a hidden Markov model. S x through

S 4 represent the “hidden” internal states. The P f j transitions each represent
the probability of going from state Sj to state Sj. These probabilities are
determined by the system learning from training data (including during
actual use). A new sequence (such as a new spoken utterance) is matched
against these probabilities to determine the likelihood that this model
produced the sequence.

Markov’s model included probabilities of each state’s successfully
occurring. He went on to hypothesize a situation in which a system has such a
hierarchy of linear sequences of states, but those are unable to be directly
examined—hence the name hidden Markov models. The lowest level of the
hierarchy emits signals, which are all we are allowed to see. Markov provides a
mathematical technique to compute what the probabilities of each transition
must be based on the observed output. The method was subsequently refined by
Norbert Wiener in 1923. Wiener’s refinement also provided a way to determine
the connections in the Markov model; essentially any connection with too low a
probability was considered not to exist. This is essentially how the human
neocortex trims connections—if they are rarely or never used, they are
considered unlikely and are pruned away. In our case, the observed output is the
speech signal created by the person talking, and the state probabilities and
connections of the Markov model constitute the neocortical hierarchy that
produced it.

I envisioned a system in which we would take samples of human speech,
apply the hidden Markov model technique to infer a hierarchy of states with

connections and probabilities (essentially a simulated neocortex for producing
speech), and then use this inferred hierarchical network of states to recognize
new utterances. To create a speaker-independent system, we would use samples
from many different individuals to train the hidden Markov models. By adding
in the element of hierarchies to represent the hierarchical nature of information
in language, these were properly called hierarchical hidden Markov models
(HHMMs).

My colleagues at Kurzweil Applied Intelligence were skeptical that this
technique would work, given that it was a self-organizing method reminiscent of
neural nets, which had fallen out of favor and with which we had had little
success. I pointed out that the network in a neural net system is fixed and does
not adapt to the input: The weights adapt, but the connections do not. In the
Markov model system, if it was set up correctly, the system would prune unused
connections so as to essentially adapt the topology.

I established what was considered a “skunk works” project (an
organizational term for a project off the beaten path that has little in the way of
formal resources) that consisted of me, one part-time programmer, and an
electrical engineer (to create the frequency filter bank). To the surprise of my
colleagues, our effort turned out to be very successful, having succeeded in
recognizing speech comprising a large vocabulary with high accuracy.

After that experiment, all of our subsequent speech recognition efforts have
been based on hierarchical hidden Markov models. Other speech recognition
companies appeared to discover the value of this method independently, and
since the mid-1980s most work in automated speech recognition has been based
on this approach. Hidden Markov models are also used in speech synthesis—
keep in mind that our biological cortical hierarchy is used not only to recognize
input but also to produce output, for example, speech and physical movement.

HHMMs are also used in systems that understand the meaning of natural-
language sentences, which represents going up the conceptual hierarchy.

Markov states

Actual natural language

like

butterfly

Hidden Markov states and possible transitions to produce a sequence of
words in natural-language text.

To understand how the HHMM method works, we start out with a network
that consists of all the state transitions that are possible. The vector quantization
method described above is critical here, because otherwise there would be too
many possibilities to consider.

Here is a possible simplified initial topology:

“one” - W-AX-N

a

HMM for

“W”

HMM for

“AX”

HMM for

“ M ”

O

o

“two" — T-OO

Entry node

/ \ / y

\ ▼ \ ▼

Exit node

rS-r>>rS

HMM for HMM for

“T” “00”

A simple hidden Markov model topology to recognize two spoken
words.

Sample utterances are processed one by one. For each, we iteratively
modify the probabilities of the transitions to better reflect the input sample we
have just processed. The Markov models used in speech recognition code the
likelihood that specific patterns of sound are found in each phoneme, how the
phonemes influence one another, and the likely orders of phonemes. The system
can also include probability networks on higher levels of language structure,
such as the order of words, the inclusion of phrases, and so on up the hierarchy
of language.

Whereas our previous speech recognition systems incorporated specific
rules about phoneme structures and sequences explicitly coded by human
linguists, the new HHMM-based system was not explicitly told that there are
forty-four phonemes in English, the sequences of vectors that were likely for
each phoneme, or what phoneme sequences were more likely than others. We let
the system discover these “rules” for itself from thousands of hours of
transcribed human speech data. The advantage of this approach over hand-coded
rules is that the models develop probabilistic rules of which human experts are
often not aware. We noticed that many of the rules that the system had
automatically learned from the data differed in subtle but important ways from
the rules established by human experts.

Once the network was trained, we began to attempt to recognize speech by
considering the alternate paths through the network and picking the path that was
most likely, given the actual sequence of input vectors we had seen. In other
words, if we saw a sequence of states that was likely to have produced that
utterance, we concluded that the utterance came from that cortical sequence.
This simulated HHMM-based neocortex included word labels, so it was able to
propose a transcription of what it heard.

We were then able to improve our results further by continuing to train the
network while we were using it for recognition. As we have discussed,
simultaneous recognition and learning also take place at every level in our
biological neocortical hierarchy.

Evolutionary (Genetic) Algorithms

There is another important consideration: How do we set the many parameters
that control a pattern recognition system’s functioning? These could include the
number of vectors that we allow in the vector quantization step, the initial
topology of hierarchical states (before the training phase of the hidden Markov
model process prunes them back), the recognition threshold at each level of the
hierarchy, the parameters that control the handling of the size parameters, and
many others. We can establish these based on our intuition, but the results will
be far from optimal.

We call these parameters “God parameters” because they are set prior to the
self-organizing method of determining the topology of the hidden Markov
models (or, in the biological case, before the person learns her lessons by
similarly creating connections in her cortical hierarchy). This is perhaps a
misnomer, given that these initial DNA-based design details are determined by
biological evolution, though some may see the hand of God in that process (and
while I do consider evolution to be a spiritual process, this discussion properly
belongs in chapter 9 ).

When it came to setting these “God parameters” in our simulated
hierarchical learning and recognizing system, we again took a cue from nature
and decided to evolve them—in our case, using a simulation of evolution. We
used what are called genetic or evolutionary algorithms (GAs), which include
simulated sexual reproduction and mutations.

Here is a simplified description of how this method works. First, we
determine a way to code possible solutions to a given problem. If the problem is
optimizing the design parameters for a circuit, then we define a list of all of the
parameters (with a specific number of bits assigned to each parameter) that
characterize the circuit. This list is regarded as the genetic code in the genetic
algorithm. Then we randomly generate thousands or more genetic codes. Each
such genetic code (which represents one set of design parameters) is considered
a simulated “solution” organism.

Now we evaluate each simulated organism in a simulated environment by
using a defined method to assess each set of parameters. This evaluation is a key
to the success of a genetic algorithm. In our example, we would run each
program generated by the parameters and judge it on appropriate criteria (did it
complete the task, how long did it take, and so on). The best-solution organisms

(the best designs) are allowed to survive, and the rest are eliminated.

Now we cause each of the survivors to multiply themselves until they reach
the same number of solution creatures. This is done by simulating sexual
reproduction: In other words, we create new offspring where each new creature
draws one part of its genetic code from one parent and another part from a
second parent. Usually no distinction is made between male or female
organisms; it’s sufficient to generate an offspring from any two arbitrary parents,
so we’re basically talking about same-sex marriage here. This is perhaps not as
interesting as sexual reproduction in the natural world, but the relevant point
here is having two parents. As these simulated organisms multiply, we allow
some mutation (random change) in the chromosomes to occur.

We’ve now defined one generation of simulated evolution; now we repeat
these steps for each subsequent generation. At the end of each generation we
determine how much the designs have improved (that is, we compute the
average improvement in the evaluation function over all the surviving
organisms). When the degree of improvement in the evaluation of the design
creatures from one generation to the next becomes very small, we stop this
iterative cycle and use the best design(s) in the last generation. (For an
algorithmic description of genetic algorithms, see this endnote.)—

The key to a genetic algorithm is that the human designers don’t directly
program a solution; rather, we let one emerge through an iterative process of
simulated competition and improvement. Biological evolution is smart but slow,
so to enhance its intelligence we greatly speed up its ponderous pace. The
computer is fast enough to simulate many generations in a matter of hours or
days, and we’ve occasionally had them run for as long as weeks to simulate
hundreds of thousands of generations. But we have to go through this iterative
process only once; as soon as we have let this simulated evolution run its course,
we can apply the evolved and highly refined rules to real problems in a rapid
fashion. In the case of our speech recognition systems, we used them to evolve
the initial topology of the network and other critical parameters. We thus used
two self-organizing methods: a GA to simulate the biological evolution that gave
rise to a particular cortical design, and HHMMs to simulate the cortical
organization that accompanies human learning.

Another major requirement for the success of a GA is a valid method of
evaluating each possible solution. This evaluation needs to be conducted quickly,
because it must take account of many thousands of possible solutions for each
generation of simulated evolution. GAs are adept at handling problems with too
many variables for which to compute precise analytic solutions. The design of an
engine, for example, may involve more than a hundred variables and requires

satisfying dozens of constraints; GAs used by researchers at General Electric
were able to come up with jet engine designs that met the constraints more
precisely than conventional methods.

When using GAs you must, however, be careful what you ask for. A genetic
algorithm was used to solve a block-stacking problem, and it came up with a
perfect solution...except that it had thousands of steps. The human programmers
forgot to include minimizing the number of steps in their evaluation function.

Scott Drave’s Electric Sheep project is a GA that produces art. The
evaluation function uses human evaluators in an open-source collaboration
involving many thousands of people. The art moves through time and you can
view it at electricsheep.org.

For speech recognition, the combination of genetic algorithms and hidden
Markov models worked extremely well. Simulating evolution with a GA was
able to substantially improve the performance of the HHMM networks. What
evolution came up with was far superior to our original design, which was based
on our intuition.

We then experimented with introducing a series of small variations in the
overall system. For example, we would make perturbations (minor random
changes) to the input. Another such change was to have adjacent Markov models
“leak” into one another by causing the results of one Markov model to influence
models that are “nearby.” Although we did not realize it at the time, the sorts of
adjustments we were experimenting with are very similar to the types of
modifications that occur in biological cortical structures.

At first, such changes hurt performance (as measured by accuracy of
recognition). But if we reran evolution (that is, reran the GA) with these
alterations in place, it would adapt the system accordingly, optimizing it for
these introduced modifications. In general, this would restore performance. If we
then removed the changes we had introduced, performance would be again
degraded, because the system had been evolved to compensate for the changes.
The adapted system became dependent on the changes.

One type of alteration that actually helped performance (after rerunning the
GA) was to introduce small random changes to the input. The reason for this is
the well-known “overfitting” problem in self-organizing systems. There is a
danger that such a system will overgeneralize to the specific examples contained
in the training sample. By making random adjustments to the input, the more
invariant patterns in the data survive, and the system thereby learns these deeper
patterns. This helped only if we reran the GA with the randomization feature on.

This introduces a dilemma in our understanding of our biological cortical
circuits. It had been noticed, for example, that there might indeed be a small

amount of leakage from one cortical connection to another, resulting from the
way that biological connections are formed: The electrochemistry of the axons
and dendrites is apparently subject to the electromagnetic effects of nearby
connections. Suppose we were able to run an experiment where we removed this
effect in an actual brain. That would be difficult to actually carry out, but not
necessarily impossible. Suppose we conducted such an experiment and found
that the cortical circuits worked less effectively without this neural leakage. We
might then conclude that this phenomenon was a very clever design by evolution
and was critical to the cortex’s achieving its level of performance. We might
further point out that such a result shows that the orderly model of the flow of
patterns up the conceptual hierarchy and the flow of predictions down the
hierarchy was in fact much more complicated because of this intricate influence
of connections on one another.

But that would not necessarily be an accurate conclusion. Consider our
experience with a simulated cortex based on HHMMs, in which we implemented
a modification very similar to interneuronal cross talk. If we then ran evolution
with that phenomenon in place, performance would be restored (because the
evolutionary process adapted to it). If we then removed the cross talk,
performance would be compromised again. In the biological case, evolution (that
is, biological evolution) was indeed “run” with this phenomenon in place. The
detailed parameters of the system have thereby been set by biological evolution
to be dependent on these factors, so that changing them will negatively affect
performance unless we run evolution again. Doing so is feasible in the simulated
world, where evolution only takes days or weeks, but in the biological world it
would require tens of thousands of years.

So how can we tell whether a particular design feature of the biological
neocortex is a vital innovation introduced by biological evolution—that is, one
that is instrumental to our level of intelligence—or merely an artifact that the
design of the system is now dependent on but could have evolved without? We
can answer that question simply by running simulated evolution with and
without these particular variations to the details of the design (for example, with
and without connection cross talk). We can even do so with biological evolution
if we’re examining the evolution of a colony of microorganisms where
generations are measured in hours, but it is not practical for complex organisms
such as humans. This is another one of the many disadvantages of biology.

Getting back to our work in speech recognition, we found that if we ran
evolution (that is, a GA) separately on the initial design of (1) the hierarchical
hidden Markov models that were modeling the internal structure of phonemes
and (2) the HHMMs’ modeling of the structures of words and phrases, we got

even better results. Both levels of the system were using HHMMs, but the GA
would evolve design variations between these different levels. This approach
still allowed the modeling of phenomena that occurs in between the two levels,
such as the smearing of phonemes that often happens when we string certain
words together (for example, “How are you all doing?” might become “How’re
y’all doing?”).

It is likely that a similar phenomenon took place in different biological
cortical regions, in that they have evolved small differences based on the types
of patterns they deal with. Whereas all of these regions use the same essential
neocortical algorithm, biological evolution has had enough time to fine-tune the
design of each of them to be optimal for their particular patterns. However, as I
discussed earlier, neuroscientists and neurologists have noticed substantial
plasticity in these areas, which supports the idea of a general neocortical
algorithm. If the fundamental methods in each region were radically different,
then such interchangeability among cortical regions would not be possible.

The systems we created in our research using this combination of self¬
organizing methods were very successful. In speech recognition, they were able
for the first time to handle fully continuous speech and relatively unrestricted
vocabularies. We were able to achieve a high accuracy rate on a wide variety of
speakers, accents, and dialects. The current state of the art as this book is being
written is represented by a product called Dragon Naturally Speaking (Version
11.5) for the PC from Nuance (formerly Kurzweil Computer Products). I suggest
that people try it if they are skeptical about the performance of contemporary
speech recognition—accuracies are often 99 percent or higher after a few
minutes of training on your voice on continuous speech and relatively
unrestricted vocabularies. Dragon Dictation is a simpler but still impressive free
app for the iPhone that requires no voice training. Siri, the personal assistant on
contemporary Apple iPhones, uses the same speech recognition technology with
extensions to handle natural-language understanding.

The performance of these systems is a testament to the power of
mathematics. With them we are essentially computing what is going on in the
neocortex of a speaker—even though we have no direct access to that person’s
brain—as a vital step in recognizing what the person is saying and, in the case of
systems like Siri, what those utterances mean. We might wonder, if we were to
actually look inside the speaker’s neocortex, would we see connections and
weights corresponding to the hierarchical hidden Markov models computed by
the software? Almost certainly we would not find a precise match; the neuronal
structures would invariably differ in many details compared with the models in
the computer. However, I would maintain that there must be an essential

mathematical equivalence to a high degree of precision between the actual
biology and our attempt to emulate it; otherwise these systems would not work
as well as they do.

LISP

LISP (LISt Processor) is a computer language, originally specified by AI pioneer
John McCarthy (1927-2011) in 1958. As its name suggests, LISP deals with
lists. Each LISP statement is a list of elements; each element is either another list
or an “atom,” which is an irreducible item constituting either a number or a
symbol. A list included in a list can be the list itself, hence LISP is capable of
recursion. Another way that LISP statements can be recursive is if a list includes
a list, and so on until the original list is specified. Because lists can include lists,
LISP is also capable of hierarchical processing. A list can be a conditional such
that it only “fires” if its elements are satisfied. In this way, hierarchies of such
conditionals can be used to identify increasingly abstract qualities of a pattern.

LISP became the rage in the artificial intelligence community in the 1970s
and early 1980s. The conceit of the LISP enthusiasts of the earlier decade was
that the language mirrored the way the human brain worked—that any intelligent
process could most easily and efficiently be coded in LISP. There followed a
mini-boomlet in “artificial intelligence” companies that offered LISP interpreters
and related LISP products, but when it became apparent in the mid-1980s that
LISP itself was not a shortcut to creating intelligent processes, the investment
balloon collapsed.

It turns out that the LISP enthusiasts were not entirely wrong. Essentially,
each pattern recognizer in the neocortex can be regarded as a LISP statement—
each one constitutes a list of elements, and each element can be another list. The
neocortex is therefore indeed engaged in list processing of a symbolic nature
very similar to that which takes place in a LISP program. Moreover, it processes
all 300 million LISP-like “statements” simultaneously.

However, there were two important features missing from the world of
LISP, one of which was learning. LISP programs had to be coded line by line by
human programmers. There were attempts to automatically code LISP programs
using a variety of methods, but these were not an integral part of the language’s
concept. The neocortex, in contrast, programs itself, filling its “statements” (that
is, the lists) with meaningful and actionable information from its own experience
and from its own feedback loops. This is a key principle of how the neocortex
works: Each one of its pattern recognizers (that is, each LISP-like statement) is
capable of filling in its own list and connecting itself both up and down to other
lists. The second difference is the size parameters. One could create a variant of

LISP (coded in LISP) that would allow for handling such parameters, but these
are not part of the basic language.

LISP is consistent with the original philosophy of the AI field, which was to
find intelligent solutions to problems and to code them directly in computer
languages. The first attempt at a self-organizing method that would teach itself
from experience—neural nets—was not successful because it did not provide a
means to modify the topology of the system in response to learning. The
hierarchical hidden Markov model effectively provided that through its pruning
mechanism. Today, the HHMM together with its mathematical cousins makes up
a major portion of the world of AI.

A corollary of the observation of the similarity of LISP and the list structure
of the neocortex is an argument made by those who insist that the brain is too
complicated for us to understand. These critics point out that the brain has
trillions of connections, and since each one must be there specifically by design,
they constitute the equivalent of trillions of lines of code. As we’ve seen, I’ve
estimated that there are on the order of 300 million pattern processors in the
neocortex—or 300 million lists where each element in the list is pointing to
another list (or, at the lowest conceptual level, to a basic irreducible pattern from
outside the neocortex). But 300 million is still a reasonably big number of LISP
statements and indeed is larger than any human-written program in existence.

However, we need to keep in mind that these lists are not actually specified
in the initial design of the nervous system. The brain creates these lists itself and
connects the levels automatically from its own experiences. This is the key
secret of the neocortex. The processes that accomplish this self-organization are
much simpler than the 300 million statements that constitute the capacity of the
neocortex. Those processes are specified in the genome. As I will demonstrate in
chapter 11 . the amount of unique information in the genome (after lossless
compression) as applied to the brain is about 25 million bytes, which is
equivalent to less than a million lines of code. The actual algorithmic complexity
is even less than that, as most of the 25 million bytes of genetic information
pertain to the biological needs of the neurons, and not specifically to their
information-processing capability. However, even 25 million bytes of design
information is a level of complexity we can handle.

Hierarchical Memory Systems

As I discussed in chapter 3 . Jeff Hawkins and Dileep George in 2003 and 2004
developed a model of the neocortex incorporating hierarchical lists that was
described in Hawkins and Blakeslee’s 2004 book On Intelligence. A more up-to-
date and very elegant presentation of the hierarchical temporal memory method
can be found in Dileep George’s 2008 doctoral dissertation.— Numenta has
implemented it in a system called NuPIC (Numenta Platform for Intelligent
Computing) and has developed pattern recognition and intelligent data-mining
systems for such clients as Forbes and Power Analytics Corporation. After
working at Numenta, George has started a new company called Vicarious
Systems with funding from the Founder Fund (managed by Peter Thiel, the
venture capitalist behind Facebook, and Sean Parker, the first president of
Facebook) and from Good Ventures, led by Dustin Moskovitz, cofounder of
Facebook. George reports significant progress in automatically modeling,
learning, and recognizing information with a substantial number of hierarchies.
He calls his system a “recursive cortical network” and plans applications for
medical imaging and robotics, among other fields. The technique of hierarchical
hidden Markov models is mathematically very similar to these hierarchical
memory systems, especially if we allow the HHMM system to organize its own
connections between pattern recognition modules. As mentioned earlier,
HHMMs provide for an additional important element, which is modeling the
expected distribution of the magnitude (on some continuum) of each input in
computing the probability of the existence of the pattern under consideration. I
have recently started a new company called Patterns, Inc., which intends to
develop hierarchical self-organizing neocortical models that utilize HHMMs and
related techniques for the purpose of understanding natural language. An
important emphasis will be on the ability for the system to design its own
hierarchies in a manner similar to a biological neocortex. Our envisioned system
will continually read a wide range of material such as Wikipedia and other
knowledge resources as well as listen to everything you say and watch
everything you write (if you let it). The goal is for it to become a helpful friend
answering your questions —before you even formulate them—and giving you
useful information and tips as you go through your day.

The Moving Frontier of AI: Climbing the Competence
Hierarchy

1. A long tiresome speech delivered by a frothy pie topping.

2. A garment worn by a child, perhaps aboard an operatic ship.

3. Wanted for a twelve-year crime spree of eating King Hrothgar’s
warriors; officer Beowulf has been assigned the case.

4. It can mean to develop gradually in the mind or to carry during
pregnancy.

5. National Teacher Day and Kentucky Derby Day.

6. Wordsworth said they soar but never roam.

7. Four-letter word for the iron fitting on the hoof of a horse or a card¬
dealing box in a casino.

8. In act three of an 1846 Verdi opera, this Scourge of God is stabbed to
death by his lover, Odabella.

—Examples of Jeopardy! queries, all of which Watson got correct.
Answers are: meringue harangue, pinafore, Grendel, gestate.
May, skylark, shoe. For the eighth query, Watson replied,
“What is Attila?” The host responded by saying, “Be more
specific?” Watson clarified with, “What is Attila the Hun?,”
which is correct.

The computer’s techniques for unraveling Jeopardy! clues sounded just like
mine. That machine zeroes in on key words in a clue, then combs its
memory (in Watson’s case, a 15-terabyte data bank of human knowledge)
for clusters of associations with these words. It rigorously checks the top
hits against all the contextual information it can muster: the category name;
the kind of answer being sought; the time, place, and gender hinted at in the
clue; and so on. And when it feels “sure” enough, it decides to buzz. This is
all an instant, intuitive process for a human Jeopardy! player, but I felt
convinced that under the hood my brain was doing more or less the same
thing.

—Ken Jennings, human Jeopardy! champion who lost to Watson

I, for one, welcome our new robot overlords.

—Ken Jennings, paraphrasing The Simpsons, after losing to
Watson

Oh my god. [Watson] is more intelligent than the average Jeopardy! player
in answering Jeopardy! questions. That’s impressively intelligent.

—Sebastian Thrun, former director of the Stanford AI Lab

Watson understands nothing. It’s a bigger steamroller.

—Noam Chomsky

Artificial intelligence is all around us—we no longer have our hand on the plug.
The simple act of connecting with someone via a text message, e-mail, or cell
phone call uses intelligent algorithms to route the information. Almost every
product we touch is originally designed in a collaboration between human and
artificial intelligence and then built in automated factories. If all the AI systems
decided to go on strike tomorrow, our civilization would be crippled: We
couldn’t get money from our bank, and indeed, our money would disappear;
communication, transportation, and manufacturing would all grind to a halt.
Fortunately, our intelligent machines are not yet intelligent enough to organize
such a conspiracy.

What is new in AI today is the viscerally impressive nature of publicly
available examples. For example, consider Google’s self-driving cars (which as
of this writing have gone over 200,000 miles in cities and towns), a technology
that will lead to significantly fewer crashes, increased capacity of roads,
alleviating the requirement of humans to perform the chore of driving, and many
other benefits. Driverless cars are actually already legal to operate on public
roads in Nevada with some restrictions, although widespread usage by the public
throughout the world is not expected until late in this decade. Technology that
intelligently watches the road and warns the driver of impending dangers is
already being installed in cars. One such technology is based in part on the
successful model of visual processing in the brain created by MIT’s Tomaso
Poggio. Called MobilEye, it was developed by Amnon Shashua, a former
postdoctoral student of Poggio’s. It is capable of alerting the driver to such
dangers as an impending collision or a child running in front of the car and has

recently been installed in cars by such manufacturers as Volvo and BMW.

I will focus in this section of the book on language technologies for several
reasons. Not surprisingly, the hierarchical nature of language closely mirrors the
hierarchical nature of our thinking. Spoken language was our first technology,
with written language as the second. My own work in artificial intelligence, as
this chapter has demonstrated, has been heavily focused on language. Finally,
mastering language is a powerfully leveraged capability. Watson has already
read hundreds of millions of pages on the Web and mastered the knowledge
contained in these documents. Ultimately machines will be able to master all of
the knowledge on the Web—which is essentially all of the knowledge of our
human-machine civilization.

English mathematician Alan Turing (1912-1954) based his eponymous test
on the ability of a computer to converse in natural language using text
messages.— Turing felt that all of human intelligence was embodied and
represented in language, and that no machine could pass a Turing test through
simple language tricks. Although the Turing test is a game involving written
language, Turing believed that the only way that a computer could pass it would
be for it to actually possess the equivalent of human-level intelligence. Critics
have proposed that a true test of human-level intelligence should include mastery
of visual and auditory information as well.— Since many of my own AI projects
involve teaching computers to master such sensory information as human
speech, letter shapes, and musical sounds, I would be expected to advocate the
inclusion of these forms of information in a true test of intelligence. Yet I agree
with Turing’s original insight that the text-only version of the Turing test is
sufficient. Adding visual or auditory input or output to the test would not
actually make it more difficult to pass.

One does not need to be an AI expert to be moved by the performance of
Watson on Jeopardy! Although I have a reasonable understanding of the
methodology used in a number of its key subsystems, that does not diminish my
emotional reaction to watching it— him? —perform. Even a perfect
understanding of how ah of its component systems work—which no one actually
has—would not help you to predict how Watson would actually react to a given
situation. It contains hundreds of interacting subsystems, and each of these is
considering millions of competing hypotheses at the same time, so predicting the
outcome is impossible. Doing a thorough analysis—after the fact—of Watson’s
deliberations for a single three-second query would take a human centuries.

To continue my own history, in the late 1980s and 1990s we began working
on natural-language understanding in limited domains. You could speak to one

of our products, called Kurzweil Voice, about anything you wanted, so long as it
had to do with editing documents. (For example, “Move the third paragraph on
the previous page to here.”) It worked pretty well in this limited but useful
domain. We also created systems with medical domain knowledge so that
doctors could dictate patient reports. It had enough knowledge of fields such as
radiology and pathology that it could question the doctor if something in the
report seemed unclear, and would guide the physician through the reporting
process. These medical reporting systems have evolved into a billion-dollar
business at Nuance.

Understanding natural language, especially as an extension to automatic
speech recognition, has now entered the mainstream. As of the writing of this
book, Siri, the automated personal assistant on the iPhone 4S, has created a stir
in the mobile computing world. You can pretty much ask Siri to do anything that
a self-respecting smartphone should be capable of doing (for example, “Where
can I get some Indian food around here?” or “Text my wife that I’m on my way,”
or “What do people think of the new Brad Pitt movie?”), and most of the time
Siri will comply. Siri will entertain a small amount of nonproductive chatter. If
you ask her what the meaning of life is, she will respond with “42,” which fans
of The Hitchhiker’s Guide to the Galaxy will recognize as its “answer to the
ultimate question of life, the universe, and everything.” Knowledge questions
(including the one about the meaning of life) are answered by Wolfram Alpha,
described on page 170 . There is a whole world of “chatbots” who do nothing but
engage in small talk. If you would like to talk to our chatbot named Ramona, go
to our Web site KurzweilAI.net and click on “Chat with Ramona.”

Some people have complained to me about Siri’s failure to answer certain
requests, but I often recall that these are the same people who persistently
complain about human service providers also. I sometimes suggest that we try it
together, and often it works better than they expect. The complaints remind me
of the story of the dog who plays chess. To an incredulous questioner, the dog’s
owner replies, “Yeah, it’s true, he does play chess, but his endgame is weak.”
Effective competitors are now emerging, such as Google Voice Search.

That the general public is now having conversations in natural spoken
language with their handheld computers marks a new era. It is typical that people
dismiss the significance of a first-generation technology because of its
limitations. A few years later, when the technology does work well, people still
dismiss its importance because, well, it’s no longer new. That being said, Siri
works impressively for a first-generation product, and it is clear that this
category of product is only going to get better.

Siri uses the HMM-based speech recognition technologies from Nuance.

The natural-language extensions were first developed by the DARPA-funded
“CALO” project.— Siri has been enhanced with Nuance’s own natural-language
technologies, and Nuance offers a very similar technology called Dragon Go!—

The methods used for understanding natural language are very similar to
hierarchical hidden Markov models, and indeed HHMM itself is commonly
used. Whereas some of these systems are not specifically labeled as using HMM
or HHMM, the mathematics is virtually identical. They all involve hierarchies of
linear sequences where each element has a weight, connections that are self-
adapting, and an overall system that self-organizes based on learning data.
Usually the learning continues during actual use of the system. This approach
matches the hierarchical structure of natural language—it is just a natural
extension up the conceptual ladder from parts of speech to words to phrases to
semantic structures. It would make sense to run a genetic algorithm on the
parameters that control the precise learning algorithm of this class of hierarchical
learning systems and determine the optimal algorithmic details.

Over the past decade there has been a shift in the way that these hierarchical
structures are created. In 1984 Douglas Lenat (born in 1950) started the
ambitious Cyc (for enCYClopedic) project, which aimed to create rules that
would codify everyday “commonsense” knowledge. The rules were organized in
a huge hierarchy, and each rule involved—again—a linear sequence of states.
For example, one Cyc rule might state that a dog has a face. Cyc can then link to
general rules about the structure of faces: that a face has two eyes, a nose, and a
mouth, and so on. We don’t need to have one set of rules for a dog’s face and
then another for a cat’s face, though we may of course want to put in additional
rules for ways in which dogs’ faces differ from cats’ faces. The system also
includes an inference engine: If we have rules that state that a cocker spaniel is a
dog, that dogs are animals, and that animals eat food, and if we were to ask the
inference engine whether cocker spaniels eat, the system would respond that yes,
cocker spaniels eat food. Over the next twenty years, and with thousands of
person-years of effort, over a million such rules were written and tested.
Interestingly, the language for writing Cyc rules—called CycL—is almost
identical to LISP.

Meanwhile, an opposing school of thought believed that the best approach
to natural-language understanding, and to creating intelligent systems in general,
was through automated learning from exposure to a very large number of
instances of the phenomena the system was trying to master. A powerful
example of such a system is Google Translate, which can translate to and from
fifty languages. That’s 2,500 different translation directions, although for most

language pairs, rather than translate language 1 directly into language 2, it will
translate language 1 into English and then English into language 2. That reduces
the number of translators Google needed to build to ninety-eight (plus a limited
number of non-English pairs for which there is direct translation). The Google
translators do not use grammatical rules; rather, they create vast databases for
each language pair of common translations based on large “Rosetta stone”
corpora of translated documents between two languages. For the six languages
that constitute the official languages of the United Nations, Google has used
United Nations documents, as they are published in all six languages. For less
common languages, other sources have been used.

The results are often impressive. DARPA runs annual competitions for the
best automated language translation systems for different language pairs, and
Google Translate often wins for certain pairs, outperforming systems created
directly by human linguists.

Over the past decade two major insights have deeply influenced the natural-
language-understanding field. The first has to do with hierarchies. Although the
Google approach started with association of flat word sequences from one
language to another, the inherent hierarchical nature of language has inevitably
crept into its operation. Systems that methodically incorporate hierarchical
learning (such as hierarchical hidden Markov models) provided significantly
better performance. However, such systems are not quite as automatic to build.
Just as humans need to learn approximately one conceptual hierarchy at a time,
the same is true for computerized systems, so the learning process needs to be
carefully managed.

The other insight is that hand-built rules work well for a core of common
basic knowledge. For translations of short passages, this approach often provides
more accurate results. For example, DARPA has rated rule-based Chinese-to-
English translators higher than Google Translate for short passages. For what is
called the tail of a language, which refers to the millions of infrequent phrases
and concepts used in it, the accuracy of rule-based systems approaches an
unacceptably low asymptote. If we plot natural-language-understanding
accuracy against the amount of training data analyzed, rule-based systems have
higher performance initially but level off at fairly low accuracies of about 70
percent. In sharp contrast, statistical systems can reach the high 90s in accuracy
but require a great deal of data to achieve that.

Often we need a combination of at least moderate performance on a small
amount of training data and then the opportunity to achieve high accuracies with
a more significant quantity. Achieving moderate performance quickly enables us
to put a system in the field and then to automatically collect training data as

people actually use it. In this way, a great deal of learning can occur at the same
time that the system is being used, and its accuracy will improve. The statistical
learning needs to be fully hierarchical to reflect the nature of language, which
also reflects how the human brain works.

This is also how Siri and Dragon Go! work—using rules for the most
common and reliable phenomena and then learning the “tail” of the language in
the hands of real users. When the Cyc team realized that they had reached a
ceiling of performance based on hand-coded rules, they too adopted this
approach. Hand-coded rules provide two essential functions. They offer adequate
initial accuracy, so that a trial system can be placed into widespread usage,
where it will improve automatically. Secondly, they provide a solid basis for the
lower levels of the conceptual hierarchy so that the automated learning can begin
to learn higher conceptual levels.

As mentioned above, Watson represents a particularly impressive example
of the approach of combining hand-coded rules with hierarchical statistical
learning. IBM combined a number of leading natural-language programs to
create a system that could play the natural-language game of Jeopardy! On
February 14-16, 2011, Watson competed with the two leading human players:
Brad Rutter, who had won more money than anyone else on the quiz show, and
Ken Jennings, who had previously held the Jeopardy! championship for the
record time of seventy-five days.

By way of context, I had predicted in my first book, The Age of Intelligent
Machines, written in the mid-1980s, that a computer would take the world chess
championship by 1998. I also predicted that when that happened, we would
either downgrade our opinion of human intelligence, upgrade our opinion of
machine intelligence, or downplay the importance of chess, and that if history
was a guide, we would minimize chess. Both of these things happened in 1997.
When IBM’s chess supercomputer Deep Blue defeated the reigning human
world chess champion, Garry Kasparov, we were immediately treated to
arguments that it was to be expected that a computer would win at chess because
computers are logic machines, and chess, after all, is a game of logic. Thus Deep
Blue’s victory was judged to be neither surprising nor significant. Many of its
critics went on to argue that computers would never master the subtleties of
human language, including metaphors, similes, puns, double entendres, and
humor.

Amount of Training Data

The accuracy of natural-language-understanding systems as a function
of the amount of training data. The best approach is to combine rules for the
“core” of the language and a data-based approach for the “tail” of the
language.

That is at least one reason why Watson represents such a significant
milestone: Jeopardy! is precisely such a sophisticated and challenging language
task. Typical Jeopardy! queries includes many of these vagaries of human
language. What is perhaps not evident to many observers is that Watson not only
had to master the language in the unexpected and convoluted queries, but for the
most part its knowledge was not hand-coded. It obtained that knowledge by
actually reading 200 million pages of natural-language documents, including all
of Wikipedia and other encyclopedias, comprising 4 trillion bytes of language-
based knowledge. As readers of this book are well aware, Wikipedia is not
written in LISP or CycL, but rather in natural sentences that have all of the
ambiguities and intricacies inherent in language. Watson needed to consider all 4
trillion characters in its reference material when responding to a question. (I
realize that Jeopardy! queries are answers in search of a question, but this is a
technicality—they ultimately are really questions.) If Watson can understand and
respond to questions based on 200 million pages—in three seconds!—there is
nothing to stop similar systems from reading the other billions of documents on
the Web. Indeed, that effort is now under way.

When we were developing character and speech recognition systems and
early natural-language-understanding systems in the 1970s through 1990s, we
used a methodology of incorporating an “expert manager.” We would develop
multiple systems to do the same thing but would incorporate somewhat different
approaches in each one. Some of the differences were subtle, such as variations

in the parameters controlling the mathematics of the learning algorithm. Some
variations were fundamental, such as including rule-based systems instead of
hierarchical statistical learning systems. The expert manager was itself a
software program that was programmed to learn the strengths and weaknesses of
these different systems by examining their performance in real-world situations.
It was based on the notion that these strengths were orthogonal; that is, one
system would tend to be strong where another was weak. Indeed, the overall
performance of the combined systems with the trained expert manager in charge
was far better than any of the individual systems.

Watson works the same way. Using an architecture called UIMA
(Unstructured Information Management Architecture), Watson deploys literally
hundreds of different systems—many of the individual language components in
Watson are the same ones that are used in publicly available natural-language-
understanding systems—all of which are attempting to either directly come up
with a response to the Jeopardy! query or else at least provide some
disambiguation of the query. UIMA is basically acting as the expert manager to
intelligently combine the results of the independent systems. UIMA goes
substantially beyond earlier systems, such as the one we developed in the
predecessor company to Nuance, in that its individual systems can contribute to
a result without necessarily coming up with a final answer. It is sufficient if a
subsystem helps narrow down the solution. UIMA is also able to compute how
much confidence it has in the final answer. The human brain does this also—we
are probably very confident of our response when asked for our mother’s first
name, but we are less so in coming up with the name of someone we met
casually a year ago.

Thus rather than come up with a single elegant approach to understanding
the language problem inherent in Jeopardy! the IBM scientists combined all of
the state-of-the-art language-understanding modules they could get their hands
on. Some use hierarchical hidden Markov models; some use mathematical
variants of HHMM; others use rule-based approaches to code directly a core set
of reliable rules. UIMA evaluates the performance of each system in actual use
and combines them in an optimal way. There is some misunderstanding in the
public discussions of Watson in that the IBM scientists who created it often
focus on UIMA, which is the expert manager they created. This leads to
comments by some observers that Watson has no real understanding of language
because it is difficult to identify where this understanding resides. Although the
UIMA framework also learns from its own experience, Watson’s
“understanding” of language cannot be found in UIMA alone but rather is
distributed across all of its many components, including the self-organizing

language modules that use methods similar to HHMM.

A separate part of Watson’s technology uses UIMA’s confidence estimate in
its answers to determine how to place Jeopardy! bets. While the Watson system
is specifically optimized to play this particular game, its core language- and
knowledge-searching technology can easily be adapted to other broad tasks. One
might think that less commonly shared professional knowledge, such as that in
the medical field, would be more difficult to master than the general-purpose
“common” knowledge that is required to play Jeopardy! Actually, the opposite is
the case: Professional knowledge tends to be more highly organized, structured,
and less ambiguous than its commonsense counterpart, so it is highly amenable
to accurate natural-language understanding using these techniques. As
mentioned, IBM is currently working with Nuance to adapt the Watson
technology to medicine.

The conversation that takes place when Watson is playing Jeopardy! is a
brief one: A question is posed, and Watson comes up with an answer. (Again,
technically, it comes up with a question to respond to an answer.) It does not
engage in a conversation that would require tracking all of the earlier statements
of all participants. (Siri actually does do this to a limited extent: If you ask it to
send a message to your wife, it will ask you to identify her, but it will remember
who she is for subsequent requests.) Tracking all of the information in a
conversation—a task that would clearly be required to pass the Turing test—is a
significant additional requirement but not fundamentally more difficult than
what Watson is doing already. After all, Watson has read hundreds of millions of
pages of material, which obviously includes many stories, so it is capable of
tracking through complicated sequential events. It should therefore be able to
follow its own conversations and take that into consideration in its subsequent
replies.

Another limitation of the Jeopardy! game is that the answers are generally
brief: It does not, for example, pose questions of the sort that ask contestants to
name the five primary themes of A Tale of Two Cities. To the extent that it can
find documents that do discuss the themes of this novel, a suitably modified
version of Watson should be able to respond to this. Coming up with such
themes on its own from just reading the book, and not essentially copying the
thoughts (even without the words) of other thinkers, is another matter. Doing so
would constitute a higher-level task than Watson is capable of today—it is what I
call a Turing test-level task. (That being said, I will point out that most humans
do not come up with their own original thoughts either but copy the ideas of
their peers and opinion leaders.) At any rate, this is 2012, not 2029, so I would
not expect Turing test-level intelligence yet. On yet another hand, I would point

out that evaluating the answers to questions such as finding key ideas in a novel
is itself not a straightforward task. If someone is asked who signed the
Declaration of Independence, one can determine whether or not her response is
true or false. The validity of answers to higher-level questions such as describing
the themes of a creative work is far less easily established.

It is noteworthy that although Watson’s language skills are actually
somewhat below that of an educated human, it was able to defeat the best two
Jeopardy! players in the world. It could accomplish this because it is able to
combine its language ability and knowledge understanding with the perfect
recall and highly accurate memories that machines possess. That is why we have
already largely assigned our personal, social, and historical memories to them.

Although I’m not prepared to move up my prediction of a computer passing
the Turing test by 2029, the progress that has been achieved in systems like
Watson should give anyone substantial confidence that the advent of Turing-
level AI is close at hand. If one were to create a version of Watson that was
optimized for the Turing test, it would probably come pretty close.

American philosopher John Searle (born in 1932) argued recently that
Watson is not capable of thinking. Citing his “Chinese room” thought
experiment (which I will discuss further in chapter 11 ). he states that Watson is
only manipulating symbols and does not understand the meaning of those
symbols. Actually, Searle is not describing Watson accurately, since its
understanding of language is based on hierarchical statistical processes—not the
manipulation of symbols. The only way that Searle’s characterization would be
accurate is if we considered every step in Watson’s self-organizing processes to
be “the manipulation of symbols.” But if that were the case, then the human
brain would not be judged capable of thinking either.

It is amusing and ironic when observers criticize Watson for just doing
statistical analysis of language as opposed to possessing the “true” understanding
of language that humans have. Hierarchical statistical analysis is exactly what
the human brain is doing when it is resolving multiple hypotheses based on
statistical inference (and indeed at every level of the neocortical hierarchy). Both
Watson and the human brain learn and respond based on a similar approach to
hierarchical understanding. In many respects Watson’s knowledge is far more
extensive than a human’s; no human can claim to have mastered all of
Wikipedia, which is only part of Watson’s knowledge base. Conversely, a human
can today master more conceptual levels than Watson, but that is certainly not a
permanent gap.

One important system that demonstrates the strength of computing applied
to organized knowledge is Wolfram Alpha, an answer engine (as opposed to a

search engine) developed by British mathematician and scientist Dr. Wolfram
(born 1959) and his colleagues at Wolfram Research. For example, if you ask
Wolfram Alpha (at WolframAlpha.com), “How many primes are there under a
million?” it will respond with “78,498.” It did not look up the answer, it
computed it, and following the answer it provides the equations it used. If you
attempted to get that answer using a conventional search engine, it would direct
you to links where you could find the algorithms required. You would then have
to plug those formulas into a system such as Mathematica, also developed by Dr.
Wolfram, but this would obviously require a lot more work (and understanding)
than simply asking Alpha.

Indeed, Alpha consists of 15 million lines of Mathematica code. What
Alpha is doing is literally computing the answer from approximately 10 trillion
bytes of data that have been carefully curated by the Wolfram Research staff.
You can ask a wide range of factual questions, such as “What country has the
highest GDP per person?” (Answer: Monaco, with $212,000 per person in U.S.
dollars), or “How old is Stephen Wolfram?” (Answer: 52 years, 9 months, 2 days
as of the day I am writing this). As mentioned, Alpha is used as part of Apple’s
Siri; if you ask Siri a factual question, it is handed off to Alpha to handle. Alpha
also handles some of the searches posed to Microsoft’s Bing search engine.

In a recent blog post, Dr. Wolfram reported that Alpha is now providing
successful responses 90 percent of the time.— He also reports an exponential
decrease in the failure rate, with a half-life of around eighteen months. It is an
impressive system, and uses handcrafted methods and hand-checked data. It is a
testament to why we created computers in the first place. As we discover and
compile scientific and mathematical methods, computers are far better than
unaided human intelligence in implementing them. Most of the known scientific
methods have been encoded in Alpha, along with continually updated data on
topics ranging from economics to physics. In a private conversation I had with
Dr. Wolfram, he estimated that self-organizing methods such as those used in
Watson typically achieve about an 80 percent accuracy when they are working
well. Alpha, he pointed out, is achieving about a 90 percent accuracy. Of course,
there is self-selection in both of these accuracy numbers in that users (such as
myself) have learned what kinds of questions Alpha is good at, and a similar
factor applies to the self-organizing methods. Eighty percent appears to be a
reasonable estimate of how accurate Watson is on Jeopardy! queries, but this
was sufficient to defeat the best humans.

It is my view that self-organizing methods such as I articulated in the
pattern recognition theory of mind are needed to understand the elaborate and
often ambiguous hierarchies we encounter in real-world phenomena, including

human language. An ideal combination for a robustly intelligent system would
be to combine hierarchical intelligence based on the PRTM (which I contend is
how the human brain works) with precise codification of scientific knowledge
and data. That essentially describes a human with a computer. We will enhance
both poles of intelligence in the years ahead. With regard to our biological
intelligence, although our neocortex has significant plasticity, its basic
architecture is limited by its physical constraints. Putting additional neocortex
into our foreheads was an important evolutionary innovation, but we cannot now
easily expand the size of our frontal lobes by a factor of a thousand, or even by
10 percent. That is, we cannot do so biologically, but that is exactly what we will
do technologically.

A Strategy for Creating a Mind

There are billions of neurons in our brains, but what are neurons? Just cells.
The brain has no knowledge until connections are made between neurons.
All that we know, all that we are, comes from the way our neurons are
connected.

—Tim Berners-Lee

Let’s use the observations I have discussed above to begin building a brain. We
will start by building a pattern recognizer that meets the necessary attributes.
Next we’ll make as many copies of the recognizer as we have memory and
computational resources to support. Each recognizer computes the probability
that its pattern has been recognized. In doing so, it takes into consideration the
observed magnitude of each input (in some appropriate continuum) and matches
these against the learned size and size variability parameters associated with
each input. The recognizer triggers its simulated axon if that computed
probability exceeds a threshold. This threshold and the parameters that control
the computation of the pattern’s probability are among the parameters we will
optimize with a genetic algorithm. Because it is not a requirement that every
input be active for a pattern to be recognized, this provides for autoassociative
recognition (that is, recognizing a pattern based on only part of the pattern being
present). We also allow for inhibitory signals (signals that indicate that the
pattern is less likely).

Recognition of the pattern sends an active signal up the simulated axon of
this pattern recognizer. This axon is in turn connected to one or more pattern
recognizers at the next higher conceptual level. All of the pattern recognizers
connected at the next higher conceptual level are accepting this pattern as one of
its inputs. Each pattern recognizer also sends signals down to pattern recognizers
at lower conceptual levels whenever most of a pattern has been recognized,
indicating that the rest of the pattern is “expected.” Each pattern recognizer has
one or more of these expected signal input channels. When an expected signal is
received in this way, the threshold for recognition of this pattern recognizer is
lowered (made easier).

The pattern recognizers are responsible for “wiring” themselves to other

pattern recognizers up and down the conceptual hierarchy. Note that all the
“wires” in a software implementation operate via virtual links (which, like Web
links, are basically memory pointers) and not actual wires. This system is
actually much more flexible than that in the biological brain. In a human brain,
new patterns have to be assigned to an actual physical pattern recognizer, and
new connections have to be made with an actual axon-to-dendrite link. Usually
this means taking an existing physical connection that is approximately what is
needed and then growing the necessary axon and dendrite extensions to
complete the full connection.

Another technique used in biological mammalian brains is to start with a
large number of possible connections and then prune the neural connections that
are not used. If a biological neocortex reassigns cortical pattern recognizers that
have already learned older patterns in order to learn more recent material, then
the connections need to be physically reconfigured. Again, these tasks are much
simpler in a software implementation. We simply assign new memory locations
to a new pattern recognizer and use memory links for the connections. If the
digital neocortex wishes to reassign cortical memory resources from one set of
patterns to another, it simply returns the old pattern recognizers to memory and
then makes the new assignment. This sort of “garbage collection” and
reassignment of memory is a standard feature of the architecture of many
software systems. In our digital brain we would also back up old memories
before discarding them from the active neocortex, a precaution we can’t take in
our biological brains.

There are a variety of mathematical techniques that can be employed to
implement this approach to self-organizing hierarchical pattern recognition. The
method I would use is hierarchical hidden Markov models, for several reasons.
From my personal perspective, I have several decades of familiarity with this
method, having used it in the earliest speech recognition and natural-language
systems starting in the 1980s. From the perspective of the overall field, there is
greater experience with hidden Markov models than with any other approach for
pattern recognition tasks. They are also extensively used in natural-language
understanding. Many NLU systems use techniques that are at least
mathematically similar to HHMM.

Note that not all hidden Markov model systems are fully hierarchical. Some
allow for just a few levels of hierarchy—for example, going from acoustic states
to phonemes to words. To build a brain, we will want to enable our system to
create as many new levels of hierarchy as needed. Also, most hidden Markov
model systems are not fully self-organizing. Some have fixed connections,
although these systems do effectively prune many of their starting connections

by allowing them to evolve zero connection weights. Our systems from the
1980s and 1990s automatically pruned connections with connection weights
below a certain level and also allowed for making new connections to better
model the training data and to learn on the fly. A key requirement, I believe, is to
allow for the system to flexibly create its own topologies based on the patterns it
is exposed to while learning. We can use the mathematical technique of linear
programming to optimally assign connections to new pattern recognizers.

Our digital brain will also accommodate substantial redundancy of each
pattern, especially ones that occur frequently. This allows for robust recognition
of common patterns and is also one of the key methods to achieving invariant
recognition of different forms of a pattern. We will, however, need rules for how
much redundancy to permit, as we don’t want to use up excessive amounts of
memory on very common low-level patterns.

The rules regarding redundancy, recognition thresholds, and the effect on
the threshold of a “this pattern is expected” indication are a few examples of key
overall parameters that affect the performance of this type of self-organizing
system. I would initially set these parameters based on my intuition, but we
would then optimize them using a genetic algorithm.

A very important consideration is the education of a brain, whether a
biological or a software one. As I discussed earlier, a hierarchical pattern
recognition system (digital or biological) will only learn about two—preferably
one—hierarchical levels at a time. To bootstrap the system I would start with
previously trained hierarchical networks that have already learned their lessons
in recognizing human speech, printed characters, and natural-language
structures. Such a system would be capable of reading natural-language
documents but would only be able to master approximately one conceptual level
at a time. Previously learned levels would provide a relatively stable basis to
learn the next level. The system can read the same documents over and over,
gaining new conceptual levels with each subsequent reading, similar to the way
people reread and achieve a deeper understanding of texts. Billions of pages of
material are available on the Web. Wikipedia itself has about four million articles
in the English version.

I would also provide a critical thinking module, which would perform a
continual background scan of all of the existing patterns, reviewing their
compatibility with the other patterns (ideas) in this software neocortex. We have
no such facility in our biological brains, which is why people can hold
completely inconsistent thoughts with equanimity. Upon identifying an
inconsistent idea, the digital module would begin a search for a resolution,
including its own cortical structures as well as all of the vast literature available

to it. A resolution might simply mean determining that one of the inconsistent
ideas is simply incorrect (if contraindicated by a preponderance of conflicting
data). More constructively, it would find an idea at a higher conceptual level that
resolves the apparent contradiction by providing a perspective that explains each
idea. The system would add this resolution as a new pattern and link to the ideas
that initially triggered the search for the resolution. This critical thinking module
would run as a continual background task. It would be very beneficial if human
brains did the same thing.

I would also provide a module that identifies open questions in every
discipline. As another continual background task, it would search for solutions to
them in other disparate areas of knowledge. As I noted, the knowledge in the
neocortex consists of deeply nested patterns of patterns and is therefore entirely
metaphorical. We can use one pattern to provide a solution or insight in an
apparently disconnected field.

As an example, recall the metaphor I used in chapter 4 relating the random
movements of molecules in a gas to the random movements of evolutionary
change. Molecules in a gas move randomly with no apparent sense of direction.
Despite this, virtually every molecule in a gas in a beaker, given sufficient time,
will leave the beaker. I noted that this provides a perspective on an important
question concerning the evolution of intelligence. Like molecules in a gas,
evolutionary changes also move every which way with no apparent direction.
Yet we nonetheless see a movement toward greater complexity and greater
intelligence, indeed to evolution’s supreme achievement of evolving a neocortex
capable of hierarchical thinking. So we are able to gain an insight into how an
apparently purposeless and directionless process can achieve an apparently
purposeful result in one field (biological evolution) by looking at another field
(thermodynamics).

I mentioned earlier how Charles Lyell’s insight that minute changes to rock
formations by streaming water could carve great valleys over time inspired
Charles Darwin to make a similar observation about continual minute changes to
the characteristics of organisms within a species. This metaphor search would be
another continual background process.

We should provide a means of stepping through multiple lists
simultaneously to provide the equivalent of structured thought. A list might be
the statement of the constraints that a solution to a problem must satisfy. Each
step can generate a recursive search through the existing hierarchy of ideas or a
search through available literature. The human brain appears to be able to handle
only four simultaneous lists at a time (without the aid of tools such as
computers), but there is no reason for an artificial neocortex to have such a

limitation.

We will also want to enhance our artificial brains with the kind of
intelligence that computers have always excelled in, which is the ability to
master vast databases accurately and implement known algorithms quickly and
efficiently. Wolfram Alpha uniquely combines a great many known scientific
methods and applies them to carefully collected data. This type of system is also
going to continue to improve given Dr. Wolfram’s observation of an exponential
decline in error rates.

Finally, our new brain needs a purpose. A purpose is expressed as a series
of goals. In the case of our biological brains, our goals are established by the
pleasure and fear centers that we have inherited from the old brain. These
primitive drives were initially set by biological evolution to foster the survival of
species, but the neocortex has enabled us to sublimate them. Watson’s goal was
to respond to Jeopardy! queries. Another simply stated goal could be to pass the
Turing test. To do so, a digital brain would need a human narrative of its own
fictional story so that it can pretend to be a biological human. It would also have
to dumb itself down considerably, for any system that displayed the knowledge
of, say, Watson would be quickly unmasked as nonbiological.

More interestingly, we could give our new brain a more ambitious goal,
such as contributing to a better world. A goal along these lines, of course, raises
a lot of questions: Better for whom? Better in what way? For biological humans?
For all conscious beings? If that is the case, who or what is conscious?

As nonbiological brains become as capable as biological ones of effecting
changes in the world—indeed, ultimately far more capable than unenhanced
biological ones—we will need to consider their moral education. A good place to
start would be with one old idea from our religious traditions: the golden rule.

THE MIND AS COMPUTER

Shaped a little like a loaf of French country bread, our brain is a crowded
chemistry lab, bustling with nonstop neural conversations. Imagine the
brain, that shiny mound of being, that mouse-gray parliament of cells, that
dream factory, that petit tyrant inside a ball of bone, that huddle of neurons
calling all the plays, that little everywhere, that fickle pleasuredome, that
wrinkled wardrobe of selves stuffed into the skull like too many clothes into
a gym bag.

—Diane Ackerman

Brains exist because the distribution of resources necessary for survival and
the hazards that threaten survival vary in space and time.

—John M. Allman

The modern geography of the brain has a deliciously antiquated feel to it—
rather like a medieval map with the known world encircled by terra
incognita where monsters roam.

—David Bainbridge

In mathematics you don’t understand things. You just get used to them.

—John von Neumann

E ver since the emergence of the computer in the middle of the twentieth
century, there has been ongoing debate not only about the ultimate extent of its
abilities but about whether the human brain itself could be considered a form of
computer. As far as the latter question was concerned, the consensus has veered
from viewing these two kinds of information-processing entities as being

essentially the same to their being fundamentally different. So is the brain a
computer?

When computers first became a popular topic in the 1940s, they were
immediately regarded as thinking machines. The ENIAC, which was announced
in 1946, was described in the press as a “giant brain.” As computers became
commercially available in the following decade, ads routinely referred to them as
brains capable of feats that ordinary biological brains could not match.

eoo
o o o

ample as a deek ccdcMiatf/r..

as ax electronic brain*

A 1957 ad showing the popular conception of a computer as a giant
brain.

Computer programs quickly enabled the machines to live up to this billing.
The “general problem solver,” created in 1959 by Herbert A. Simon, J. C. Shaw,
and Allen Newell at Carnegie Mellon University, was able to devise a proof to a
theorem that mathematicians Bertrand Russell (1872-1970) and Alfred North
Whitehead (1861-1947) had been unable to solve in their famous 1913 work
Principia Mathematica. What became apparent in the decades that followed was
that computers could readily significantly exceed unassisted human capability in
such intellectual exercises as solving mathematical problems, diagnosing
disease, and playing chess but had difficulty with controlling a robot tying
shoelaces or with understanding the commonsense language that a five-year-old
child could comprehend. Computers are only now starting to master these sorts

of skills. Ironically, the evolution of computer intelligence has proceeded in the
opposite direction of human maturation.

The issue of whether or not the computer and the human brain are at some
level equivalent remains controversial today. In the introduction I mentioned that
there were millions of links for quotations on the complexity of the human brain.
Similarly, a Google inquiry for “Quotations: the brain is not a computer” also
returns millions of links. In my view, statements along these lines are akin to
saying, “Applesauce is not an apple.” Technically that statement is true, but you
can make applesauce from an apple. Perhaps more to the point, it is like saying,
“Computers are not word processors.” It is true that a computer and a word
processor exist at different conceptual levels, but a computer can become a word
processor if it is running word processing software and not otherwise. Similarly,
a computer can become a brain if it is running brain software. That is what
researchers including myself are attempting to do.

The question, then, is whether or not we can find an algorithm that would
turn a computer into an entity that is equivalent to a human brain. A computer,
after all, can run any algorithm that we might define because of its innate
universality (subject only to its capacity). The human brain, on the other hand, is
running a specific set of algorithms. Its methods are clever in that it allows for
significant plasticity and the restructuring of its own connections based on its
experience, but these functions can be emulated in software.

The universality of computation (the concept that a general-purpose
computer can implement any algorithm)—and the power of this idea—emerged
at the same time as the first actual machines. There are four key concepts that
underlie the universality and feasibility of computation and its applicability to
our thinking. They are worth reviewing here, because the brain itself makes use
of them. The first is the ability to communicate, remember, and compute
information reliably. Around 1940, if you used the word “computer,” people
assumed you were talking about an analog computer, in which numbers were
represented by different levels of voltage, and specialized components could
perform arithmetic functions such as addition and multiplication. A big
limitation of analog computers, however, was that they were plagued by
accuracy issues. Numbers could only be represented with an accuracy of about
one part in a hundred, and as voltage levels representing them were processed by
increasing numbers of arithmetic operators, errors would accumulate. If you
wanted to perform more than a handful of computations, the results would
become so inaccurate as to be meaningless.

Anyone who can remember the days of recording music with analog tape
machines will recall this effect. There was noticeable degradation on the first

copy, as it was a little noisier than the original. (Remember that “noise”
represents random inaccuracies.) A copy of the copy was noisier still, and by the
tenth generation the copy was almost entirely noise. It was assumed that the
same problem would plague the emerging world of digital computers. We can
understand such concerns if we consider the communication of digital
information through a channel. No channel is perfect and each one will have
some inherent error rate. Suppose we have a channel that has a .9 probability of
correctly transmitting each bit. If I send a message that is one bit long, the
probability of accurately transmitting it through that channel will be .9. Suppose
I send two bits? Now the accuracy is .9 2 = .81. How about if I send one byte
(eight bits)? I have less than an even chance (.43 to be exact) of sending it
correctly. The probability of accurately sending five bytes is about 1 percent.

An obvious solution to circumvent this problem is to make the channel
more accurate. Suppose the channel makes only one error in a million bits. If I
send a file consisting of a half million bytes (about the size of a modest program
or database), the probability of correctly transmitting it is less than 2 percent,
despite the very high inherent accuracy of the channel. Given that a single-bit
error can completely invalidate a computer program and other forms of digital
data, that is not a satisfactory situation. Regardless of the accuracy of the
channel, since the likelihood of an error in a transmission grows rapidly with the
size of the message, this would seem to be an intractable barrier.

Analog computers approached this problem through graceful degradation
(meaning that users only presented problems in which they could tolerate small
errors); however, if users of analog computers limited themselves to a
constrained set of calculations, the computers did prove somewhat useful.
Digital computers, on the other hand, require continual communication, not just
from one computer to another, but within the computer itself. There is
communication from its memory to and from the central processing unit. Within
the central processing unit, there is communication from one register to another
and back and forth to the arithmetic unit, and so forth. Even within the arithmetic
unit, there is communication from one bit register to another. Communication is
pervasive at every level. If we consider that error rates escalate rapidly with
increased communication and that a single-bit error can destroy the integrity of a
process, digital computation was doomed—or so it seemed at the time.

Remarkably, that was the common view until American mathematician
Claude Shannon (1916-2001) came along and demonstrated how we can create
arbitrarily accurate communication using even the most unreliable
communication channels. What Shannon stated in his landmark paper “A
Mathematical Theory of Communication,” published in the Bell System

Technical Journal in July and October 1948, and in particular in his noisy
channel-coding theorem, was that if you have available a channel with any error
rate (except for exactly 50 percent per bit, which would mean that the channel
was just transmitting pure noise), you are able to transmit a message in which
the error rate is as accurate as you desire. In other words, the error rate of the
transmission can be one bit out of n bits, where n can be as large as you define.
So, for example, in the extreme, if you have a channel that correctly transmits
bits of information only 51 percent of the time (that is, it transmits the correct bit
just slightly more often than the wrong bit), you can nonetheless transmit
messages such that only one bit out of a million is incorrect, or one bit out of a
trillion or a trillion trillion.

How is this possible? The answer is through redundancy. That may seem
obvious now, but it was not at the time. As a simple example, if I transmit each
bit three times and take the majority vote, I will have substantially increased the
reliability of the result. If that is not good enough, simply increase the
redundancy until you get the reliability you need. Simply repeating information
is the easiest way to achieve arbitrarily high accuracy rates from low-accuracy
channels, but it is not the most efficient approach. Shannon’s paper, which
established the field of information theory, presented optimal methods of error
detection and correction codes that can achieve any target accuracy through any
nonrandom channel.

Older readers will recall telephone modems, which transmitted information
through noisy analog phone lines. These lines featured audibly obvious hisses
and pops and many other forms of distortion, but nonetheless were able to
transmit digital data with very high accuracy rates, thanks to Shannon’s noisy
channel theorem. The same issue and the same solution exist for digital memory.
Ever wonder how CDs, DVDs, and program disks continue to provide reliable
results even after the disk has been dropped on the floor and scratched? Again,
we can thank Shannon.

Computation consists of three elements: communication—which, as I
mentioned, is pervasive both within and between computers—memory, and logic
gates (which perform the arithmetic and logical functions). The accuracy of logic
gates can also be made arbitrarily high by similarly using error detection and
correction codes. It is due to Shannon’s theorem and theory that we can handle
arbitrarily large and complex digital data and algorithms without the processes
being disturbed or destroyed by errors. It is important to point out that the brain
uses Shannon’s principle as well, although the evolution of the human brain
clearly predates Shannon’s own! Most of the patterns or ideas (and an idea is
also a pattern), as we have seen, are stored in the brain with a substantial amount

of redundancy. A primary reason for the redundancy in the brain is the inherent
unreliability of neural circuits.

The second important idea on which the information age relies is the one I
mentioned earlier: the universality of computation. In 1936 Alan Turing
described his “Turing machine,” which was not an actual machine but another
thought experiment. His theoretical computer consists of an infinitely long
memory tape with a 1 or a 0 in each square. Input to the machine is presented on
this tape, which the machine can read one square at a time. The machine also
contains a table of rules—essentially a stored program—that consist of
numbered states. Each rule specifies one action if the square currently being read
is a 0, and a different action if the current square is a 1. Possible actions include
writing a 0 or 1 on the tape, moving the tape one square to the right or left, or
halting. Each state will then specify the number of the next state that the
machine should be in.

The input to the Turing machine is presented on the tape. The program runs,
and when the machine halts, it has completed its algorithm, and the output of the
process is left on the tape. Note that even though the tape is theoretically infinite
in length, any actual program that does not get into an infinite loop will use only
a finite portion of the tape, so if we limit ourselves to a finite tape, the machine
will still solve a useful set of problems.

If the Turing machine sounds simple, it is because that was its inventor’s
objective. Turing wanted his machine to be as simple as possible (but no simpler,
to paraphrase Einstein). Turing and Alonzo Church (1903-1995), his former
professor, went on to develop the Church-Turing thesis, which states that if a
problem that can be presented to a Turing machine is not solvable by it, it is also
not solvable by any machine, following natural law. Even though the Turing
machine has only a handful of commands and processes only one bit at a time, it
can compute anything that any computer can compute. Another way to say this
is that any machine that is “Turing complete” (that is, that has equivalent
capabilities to a Turing machine) can compute any algorithm (any procedure that
we can define).

State transition diagram

0

1

0

1

0

1

0

1

0

Infinite tape

A block diagram of a Turing machine with a head that reads and writes

the tape and an internal program consisting of state transitions.

“Strong” interpretations of the Church-Turing thesis propose an essential
equivalence between what a human can think or know and what is computable
by a machine. The basic idea is that the human brain is likewise subject to
natural law, and thus its information-processing ability cannot exceed that of a
machine (and therefore of a Turing machine).

We can properly credit Turing with establishing the theoretical foundation
of computation with his 1936 paper, but it is important to note that he was deeply
influenced by a lecture that Hungarian American mathematician John von
Neumann (1903-1957) gave in Cambridge in 1935 on his stored program
concept, a concept enshrined in the Turing machine.- In turn, von Neumann was
influenced by Turing’s 1936 paper, which elegantly laid out the principles of
computation, and made it required reading for his colleagues in the late 1930s
and early 1940s. 2

In the same paper Turing reports another unexpected discovery: that of
unsolvable problems. These are problems that are well defined with unique
answers that can be shown to exist, but that we can also prove can never be
computed by any Turing machine—that is to say, by any machine, a reversal of
what had been a nineteenth-century dogma that problems that could be defined
would ultimately be solved. Turing showed that there are as many unsolvable
problems as solvable ones. Austrian American mathematician and philosopher
Kurt Godel reached a similar conclusion in his 1931 “incompleteness theorem.”
We are thus left with the perplexing situation of being able to define a problem,
to prove that a unique answer exists, and yet know that the answer can never be
found.

Turing had shown that at its essence, computation is based on a very simple
mechanism. Because the Turing machine (and therefore any computer) is

capable of basing its future course of action on results it has already computed, it
is capable of making decisions and modeling arbitrarily complex hierarchies of
information.

In 1939 Turing designed an electronic calculator called Bombe that helped
decode messages that had been encrypted by the Nazi Enigma coding machine.
By 1943, an engineering team influenced by Turing completed what is arguably
the first computer, the Colossus, that enabled the Allies to continue decoding
messages from more sophisticated versions of Enigma. The Bombe and
Colossus were designed for a single task and could not be reprogrammed for a
different one. But they performed this task brilliantly and are credited with
having enabled the Allies to overcome the three-to-one advantage that the
German Luftwaffe enjoyed over the British Royal Air Force and win the crucial
Battle of Britain, as well as to continue anticipating Nazi tactics throughout the
war.

It was on these foundations that John von Neumann created the architecture
of the modern computer, which represents our third major idea. Called the von
Neumann machine, it has remained the core structure of essentially every
computer for the past sixty-seven years, from the microcontroller in your
washing machine to the largest supercomputers. In a paper dated June 30, 1945,
and titled “First Draft of a Report on the EDVAC,” von Neumann presented the
ideas that have dominated computation ever since.- The von Neumann model
includes a central processing unit, where arithmetical and logical operations are
carried out; a memory unit, where the program and data are stored; mass storage;
a program counter; and input/output channels. Although this paper was intended
as an internal project document, it has become the bible for computer designers.
You never know when a seemingly routine internal memo will end up
revolutionizing the world.

The Turing machine was not designed to be practical. Turing’s theorems
were concerned not with the efficiency of solving problems but rather in
examining the range of problems that could in theory be solved by computation.
Von Neumann’s goal, on the other hand, was to create a feasible concept of a
computational machine. His model replaces Turing’s one-bit computations with
multiple-bit words (generally some multiple of eight bits). Turing’s memory tape
is sequential, so Turing machine programs spend an inordinate amount of time
moving the tape back and forth to store and retrieve intermediate results. In
contrast, von Neumann’s memory is random access, so that any data item can be
immediately retrieved.

One of von Neumann’s key ideas is the stored program, which he had
introduced a decade earlier: placing the program in the same type of random

access memory as the data (and often in the same block of memory). This allows
the computer to be reprogrammed for different tasks as well as for self¬
modifying code (if the program store is writable), which enables a powerful
form of recursion. Up until that time, virtually all computers, including the
Colossus, were built for a specific task. The stored program makes it possible for
a computer to be truly universal, thereby fulfilling Turing’s vision of the
universality of computation.

Another key aspect of the von Neumann machine is that each instruction
includes an operation code specifying the arithmetic or logical operation to be
performed and the address of an operand from memory.

Von Neumann’s concept of how a computer should be architected was
introduced with his publication of the design of the ED VAC, a project he
conducted with collaborators J. Presper Eckert and John Mauchly. The EDVAC
itself did not actually run until 1951, by which time there were other stored-
program computers, such as the Manchester Small-Scale Experimental Machine,
ENIAC, EDSAC, and BINAC, all of which had been deeply influenced by von
Neumann’s paper and involved Eckert and Mauchly as designers. Von Neumann
was a direct contributor to the design of a number of these machines, including a
later version of ENIAC, which supported a stored program.

There were a few precursors to von Neumann’s architecture, although with
one surprising exception, none are true von Neumann machines. In 1944
Howard Aiken introduced the Mark I, which had an element of programmability
but did not use a stored program. It read instructions from a punched paper tape
and then executed each command immediately. It also lacked a conditional
branch instruction.

In 1941 German scientist Konrad Zuse (1910-1995) created the Z-3
computer. It also read its program from a tape (in this case, coded on film) and
also had no conditional branch instruction. Interestingly, Zuse had support from
the German Aircraft Research Institute, which used the device to study wing
flutter, but his proposal to the Nazi government for funding to replace his relays
with vacuum tubes was turned down. The Nazis deemed computation as “not
war important.” That perspective goes a long way, in my view, toward
explaining the outcome of the war.

There is actually one genuine forerunner to von Neumann’s concept, and it
comes from a full century earlier! English mathematician and inventor Charles
Babbage’s (1791-1871) Analytical Engine, which he first described in 1837, did
incorporate von Neumann’s ideas and featured a stored program via punched
cards borrowed from the Jacquard loom.- Its random access memory included
1,000 words of 50 decimal digits each (the equivalent of about 21 kilobytes).

Each instruction included an op code and an operand number, just like modern
machine languages. It did include conditional branching and looping, so it was a
true von Neumann machine. It was based entirely on mechanical gears and it
appears that the Analytical Engine was beyond Babbage’s design and
organizational skills. He built parts of it but it never ran. It is unclear whether the
twentieth-century pioneers of the computer, including von Neumann, were aware
of Babbage’s work.

Babbage’s computer did result in the creation of the field of software
programming. English writer Ada Byron (1815-1852), Countess of Lovelace
and the only legitimate child of the poet Lord Byron, was the world’s first
computer programmer. She wrote programs for the Analytical Engine, which she
needed to debug in her own mind (since the computer never worked), a practice
well known to software engineers today as “table checking.” She translated an
article by the Italian mathematician Luigi Menabrea on the Analytical Engine
and added extensive notes of her own, writing that “the Analytical Engine
weaves algebraic patterns, just as the Jacquard loom weaves flowers and leaves.”
She went on to provide perhaps the first speculations on the feasibility of
artificial intelligence, but concluded that the Analytical Engine has “no
pretensions whatever to originate anything.”

Babbage’s conception is quite miraculous when you consider the era in
which he lived and worked. However, by the mid-twentieth century, his ideas
had been lost in the mists of time (although they were subsequently
rediscovered). It was von Neumann who conceptualized and articulated the key
principles of the computer as we know it today, and the world recognizes this by
continuing to refer to the von Neumann machine as the principal model of
computation. Keep in mind, though, that the von Neumann machine continually
communicates data between its various units and within these units, so it could
not be built without Shannon’s theorems and the methods he devised for
transmitting and storing reliable digital information.

That brings us to the fourth important idea, which is to go beyond Ada
Byron’s conclusion that a computer could not think creatively and find the key
algorithms employed by the brain and then use these to turn a computer into a
brain. Alan Turing introduced this goal in his 1950 paper “Computing
Machinery and Intelligence,” which includes his now-famous Turing test for
ascertaining whether or not an AI has achieved a human level of intelligence.

In 1956 von Neumann began preparing a series of lectures intended for the
prestigious Silliman lecture series at Yale University. Due to the ravages of
cancer, he never delivered these talks nor did he complete the manuscript from
which they were to be given. This unfinished document nonetheless remains a

brilliant and prophetic foreshadowing of what I regard as humanity’s most
daunting and important project. It was published posthumously as The Computer
and the Brain in 1958. It is fitting that the final work of one of the most brilliant
mathematicians of the last century and one of the pioneers of the computer age
was an examination of intelligence itself. This project was the earliest serious
inquiry into the human brain from the perspective of a mathematician and
computer scientist. Prior to von Neumann, the fields of computer science and
neuroscience were two islands with no bridge between them.

Von Neumann starts his discussion by articulating the similarities and
differences between the computer and the human brain. Given when he wrote
this manuscript, it is remarkably accurate. He noted that the output of neurons
was digital—an axon either fired or it didn’t. This was far from obvious at the
time, in that the output could have been an analog signal. The processing in the
dendrites leading into a neuron and in the soma neuron cell body, however, was
analog, and he described its calculations as a weighted sum of inputs with a
threshold. This model of how neurons work led to the field of connectionism,
which built systems based on this neuron model in both hardware and software.
(As I described in the previous chapter , the first such connectionist system was
created by Frank Rosenblatt as a software program on an IBM 704 computer at
Cornell in 1957, immediately after von Neumann’s draft lectures became
available.) We now have more sophisticated models of how neurons combine
inputs, but the essential idea of analog processing of dendrite inputs using
neurotransmitter concentrations has remained valid.

Von Neumann applied the concept of the universality of computation to
conclude that even though the architecture and building blocks appear to be
radically different between brain and computer, we can nonetheless conclude
that a von Neumann machine can simulate the processing in a brain. The
converse does not hold, however, because the brain is not a von Neumann
machine and does not have a stored program as such (albeit we can simulate a
very simple Turing machine in our heads). Its algorithm or methods are implicit
in its structure. Von Neumann correctly concludes that neurons can learn patterns
from their inputs, which we have now established are coded in part in dendrite
strengths. What was not known in von Neumann’s time is that learning also
takes place through the creation and destruction of connections between neurons.

Von Neumann presciently notes that the speed of neural processing is
extremely slow, on the order of a hundred calculations per second, but that the
brain compensates for this through massive parallel processing—another
unobvious and key insight. Von Neumann argued that each one of the brain’s
10 10 neurons (a tally that itself was reasonably accurate; estimates today are

between 10 10 and 10 11 ) was processing at the same time. In fact, each of the
connections (with an average of about 10 3 to 10 4 connections per neuron) is
computing simultaneously.

Von Neumann’s estimates and his descriptions of neural processing are
remarkable, given the primitive state of neuroscience at the time. One aspect of
his work that I do disagree with, however, is his assessment of the brain’s
memory capacity. He assumes that the brain remembers every input for its entire
life. Von Neumann assumes an average life span of 60 years, or about 2 x 10 9
seconds. With about 14 inputs to each neuron per second (which is actually low
by at least three orders of magnitude) and with 10 10 neurons, he arrives at an
estimate of about 10 2 ° bits for the brain’s memory capacity. The reality, as I have
noted earlier, is that we remember only a very small fraction of our thoughts and
experiences, and even these memories are not stored as bit patterns at a low level
(such as a video image), but rather as sequences of higher-level patterns.

As von Neumann describes each mechanism in the brain, he shows how a
modern computer could accomplish the same thing, despite their apparent
differences. The brain’s analog mechanisms can be simulated through digital
ones because digital computation can emulate analog values to any desired
degree of precision (and the precision of analog information in the brain is quite
low). The brain’s massive parallelism can be simulated as well, given the
significant speed advantage of computers in serial computation (an advantage
that has vastly expanded over time). In addition, we can also use parallel
processing in computers by using parallel von Neumann machines—which is
exactly how supercomputers work today.

Von Neumann concludes that the brain’s methods cannot involve lengthy
sequential algorithms, when one considers how quickly humans are able to make
decisions combined with the very slow computational speed of neurons. When a
third baseman fields a ball and decides to throw to first rather than to second
base, he makes this decision in a fraction of a second, which is only enough time
for each neuron to go through a handful of cycles. Von Neumann concludes
correctly that the brain’s remarkable powers come from all its 100 billion
neurons being able to process information simultaneously. As I have noted, the
visual cortex makes sophisticated visual judgments in only three or four neural
cycles.

There is considerable plasticity in the brain, which enables us to learn. But
there is far greater plasticity in a computer, which can completely restructure its
methods by changing its software. Thus, in that respect, a computer will be able
to emulate the brain, but the converse is not the case.

When von Neumann compared the capacity of the brain’s massively
parallel organization to the (few) computers of his time, it was clear that the
brain had far greater memory and speed. By now the first supercomputer to
achieve specifications matching some of the more conservative estimates of the
speed required to functionally simulate the human brain (about 10 16 operations
per second) has been built.- (I estimate that this level of computation will cost
$1,000 by the early 2020s.) With regard to memory we are even closer. Even
though it was remarkably early in the history of the computer when his
manuscript was written, von Neumann nonetheless had confidence that both the
hardware and software of human intelligence would ultimately fall into place,
which was his motivation for having prepared these lectures.

Von Neumann was deeply aware of the increasing pace of progress and its
profound implications for humanity’s future. A year after his death in 1957,
fellow mathematician Stan Ulam quoted him as having said in the early 1950s
that “the ever accelerating progress of technology and changes in the mode of
human life give the appearance of approaching some essential singularity in the
history of the race beyond which human affairs, as we know them, could not
continue.” This is the first known use of the word “singularity” in the context of
human technological history.

Von Neumann’s fundamental insight was that there is an essential
equivalence between a computer and the brain. Note that the emotional
intelligence of a biological human is part of its intelligence. If von Neumann’s
insight is correct, and if one accepts my own leap of faith that a nonbiological
entity that convincingly re-creates the intelligence (emotional and otherwise) of
a biological human is conscious (see the next chapter !, then one would have to
conclude that there is an essential equivalence between a computer—with the
right software —and a (conscious) mind. So is von Neumann correct?

Most computers today are entirely digital, whereas the human brain
combines digital and analog methods. But analog methods are easily and
routinely re-created by digital ones to any desired level of accuracy. American
computer scientist Carver Mead (born in 1934) has shown that we can directly
emulate the brain’s analog methods in silicon, which he has demonstrated with
what he calls “neuromorphic” chips.- Mead has demonstrated how this approach
can be thousands of times more efficient than digitally emulating analog
methods. As we codify the massively repeated neocortical algorithm, it will
make sense to use Mead’s approach. The IBM Cognitive Computing Group, led
by Dharmendra Modha, has introduced chips that emulate neurons and their
connections, including the ability to form new connections. 2 Called

“SyNAPSE,” one of the chips provides a direct simulation of 256 neurons with
about a quarter million synaptic connections. The goal of the project is to create
a simulated neocortex with 10 billion neurons and 100 trillion connections—
close to a human brain—that uses only one kilowatt of power.

As von Neumann described over a half century ago, the brain is extremely
slow but massively parallel. Today’s digital circuits are at least 10 million times
faster than the brain’s electrochemical switches. Conversely, all 300 million of
the brain’s neocortical pattern recognizers process simultaneously, and all
quadrillion of its interneuronal connections are potentially computing at the
same time. The key issue for providing the requisite hardware to successfully
model a human brain, though, is the overall memory and computational
throughput required. We do not need to directly copy the brain’s architecture,
which would be a very inefficient and inflexible approach.

Let’s estimate what those hardware requirements are. Many projects have
attempted to emulate the type of hierarchical learning and pattern recognition
that takes place in the neocortical hierarchy, including my own work with
hierarchical hidden Markov models. A conservative estimate from my own
experience is that emulating one cycle in a single pattern recognizer in the
biological brain’s neocortex would require about 3,000 calculations. Most
simulations run at a fraction of this estimate. With the brain mnning at about 10 2
(100) cycles per second, that comes to 3 x 10 5 (300,000) calculations per second
per pattern recognizer. Using my estimate of 3 x 10 8 (300 million) pattern
recognizers, we get about 10 14 (100 trillion) calculations per second, a figure
that is consistent with my estimate in The Singularity Is Near. In that book I
projected that to functionally simulate the brain would require between 10 14 and
10 16 calculations per second (cps) and used 10 16 cps to be conservative. AI
expert Hans Moravec’s estimate, based on extrapolating the computational
requirement of the early (initial) visual processing across the entire brain, is 10 14
cps, which matches my own assessment here.

Routine desktop machines can reach 10 10 cps, although this level of
performance can be significantly amplified by using cloud resources. The fastest
supercomputer, Japan’s K Computer, has already reached 10 16 cps.- Given that
the algorithm of the neocortex is massively repeated, the approach of using
neuromorphic chips such as the IBM SyNAPSE chips mentioned above is also
promising.

In terms of memory requirement, we need about 30 bits (about four bytes)
for one connection to address one of 300 million other pattern recognizers. If we
estimate an average of eight inputs to each pattern recognizer, that comes to 32

bytes per recognizer. If we add a one-byte weight for each input, that brings us to
40 bytes. Add another 32 bytes for downward connections, and we are at 72
bytes. Note that the branching-up-and-down figure will often be much higher
than eight, though these very large branching trees are shared by many
recognizers. For example, there may be hundreds of recognizers involved in
recognizing the letter “p.” These will feed up into thousands of such recognizers
at this next higher level that deal with words and phrases that include “p.”
However, each “p” recognizer does not repeat the tree of connections that feeds
up to all of the words and phrases that include “p”—they all share one such tree
of connections. The same is true of downward connections: A recognizer that is
responsible for the word “APPLE” will tell all of the thousands of “E”
recognizers at a level below it that an “E” is expected if it has already seen “A,”
“P,” “P,” and “L.” That tree of connections is not repeated for each word or
phrase recognizer that wants to inform the next lower level that an “E” is
expected. Again, they are shared. For this reason, an overall estimate of eight up
and eight down on average per pattern recognizer is reasonable. Even if we
increase this particular estimate, it does not significantly change the order of
magnitude of the resulting estimate.

With 3 x io 8 (300 million) pattern recognizers at 72 bytes each, we get an
overall memory requirement of about 2 x IO 10 (20 billion) bytes. That is actually
a quite modest number that routine computers today can exceed.

These estimates are intended only to provide rough estimates of the order of
magnitude required. Given that digital circuits are inherently about 10 million
times faster than the biological neocortical circuits, we do not need to match the
human brain for parallelism—modest parallel processing (compared with the
trillions-fold parallelism of the human brain) will be sufficient. We can see that
the necessary computational requirements are coming within reach. The brain’s
rewiring of itself—dendrites are continually creating new synapses—can also be
emulated in software using links, a far more flexible system than the brain’s
method of plasticity, which as we have seen is impressive but limited.

The redundancy used by the brain to achieve robust invariant results can
certainly be replicated in software emulations. The mathematics of optimizing
these types of self-organizing hierarchical learning systems is well understood.
The organization of the brain is far from optimal. Of course it didn’t need to be
—it only needed to be good enough to achieve the threshold of being able to
create tools that would compensate for its own limitations.

Another restriction of the human neocortex is that there is no process that
eliminates or even reviews contradictory ideas, which accounts for why human

thinking is often massively inconsistent. We have a weak mechanism to address
this called critical thinking, but this skill is not practiced nearly as often as it
should be. In a software-based neocortex, we can build in a process that reveals
inconsistencies for further review.

It is important to note that the design of an entire brain region is simpler
than the design of a single neuron. As discussed earlier, models often get simpler
at a higher level—consider an analogy with a computer. We do need to
understand the detailed physics of semiconductors to model a transistor, and the
equations underlying a single real transistor are complex. A digital circuit that
multiples two numbers requires hundreds of them. Yet we can model this
multiplication circuit very simply with one or two formulas. An entire computer
with billions of transistors can be modeled through its instruction set and register
description, which can be described on a handful of written pages of text and
formulas. The software programs for an operating system, language compilers,
and assemblers are reasonably complex, but modeling a particular program—for
example, a speech recognition program based on hierarchical hidden Markov
modeling—may likewise be described in only a few pages of equations.
Nowhere in such a description would be found the details of semiconductor
physics or even of computer architecture.

A similar observation holds true for the brain. A particular neocortical
pattern recognizer that detects a particular invariant visual feature (such as a
face) or that performs a bandpass filtering (restricting input to a specific
frequency range) on sound or that evaluates the temporal proximity of two
events can be described with far fewer specific details than the actual physics
and chemical relations controlling the neurotransmitters, ion channels, and other
synaptic and dendritic variables involved in the neural processes. Although all of
this complexity needs to be carefully considered before advancing to the next
higher conceptual level, much of it can be simplified as the operating principles
of the brain are revealed.

CHAPTER 9

THOUGHT EXPERIMENTS
ON THE MIND

Minds are simply what brains do.

—Marvin Minsky, The Society of Mind

When intelligent machines are constructed, we should not be surprised to
find them as confused and as stubborn as men in their convictions about
mind-matter, consciousness, free will, and the like.

—Marvin Minsky, The Society of Mind

Who Is Conscious?

The real history of consciousness starts with one’s first lie.

—Joseph Brodsky

Suffering is the sole origin of consciousness.

—Fyodor Dostoevsky, Notes from Underground

There is a kind of plant that eats organic food with its flowers: when a fly
settles upon the blossom, the petals close upon it and hold it fast till the
plant has absorbed the insect into its system; but they will close on nothing
but what is good to eat; of a drop of rain or a piece of stick they will take no
notice. Curious! that so unconscious a thing should have such a keen eye to
its own interest. If this is unconsciousness, where is the use of
consciousness?

—Samuel Butler, 1871

w e have been examining the brain as an entity that is capable of certain levels
of accomplishment. But that perspective essentially leaves our selves out of the
picture. We appear to live in our brains. We have subjective lives. How does the
objective view of the brain that we have discussed up until now relate to our own
feelings, to our sense of being the person having the experiences?

British philosopher Colin McGinn (born in 1950) writes that discussing
“consciousness can reduce even the most fastidious thinker to blabbering
incoherence.” The reason for this is that people often have unexamined and
inconsistent views on exactly what the term means.

Many observers consider consciousness to be a form of performance—for
example, the capacity for self-reflection, that is, the ability to understand one’s
own thoughts and to explain them. I would describe that as the ability to think
about one’s own thinking. Presumably, we could come up with a way of
evaluating this ability and then use this test to separate conscious things from
unconscious things.

However, we quickly get into trouble in trying to implement this approach.
Is a baby conscious? A dog? They’re not very good at describing their own
thinking process. There are people who believe that babies and dogs are not
conscious beings precisely because they cannot explain themselves. How about
the computer known as Watson? It can be put into a mode where it actually does
explain how it came up with a given answer. Because it contains a model of its
own thinking, is Watson therefore conscious whereas the baby and the dog are
not?

Before we proceed to parse this question further, it is important to reflect on
the most significant distinction relating to it: What is it that we can ascertain
from science, versus what remains truly a matter of philosophy? One view is that
philosophy is a kind of halfway house for questions that have not yet yielded to
the scientific method. According to this perspective, once science advances
sufficiently to resolve a particular set of questions, philosophers can then move
on to other concerns, until such time that science resolves them also. This view
is endemic where the issue of consciousness is concerned, and specifically the
question “What and who is conscious?”

Consider these statements by philosopher John Searle: “We know that
brains cause consciousness with specific biological mechanisms.... The essential
thing is to recognize that consciousness is a biological process like digestion,
lactation, photosynthesis, or mitosis.... The brain is a machine, a biological
machine to be sure, but a machine all the same. So the first step is to figure out
how the brain does it and then build an artificial machine that has an equally
effective mechanism for causing consciousness.”- People are often surprised to
see these quotations because they assume that Searle is devoted to protecting the
mystery of consciousness against reductionists like Ray Kurzweil.

The Australian philosopher David Chalmers (born in 1966) has coined the
term “the hard problem of consciousness” to describe the difficulty of pinning
down this essentially indescribable concept. Sometimes a brief phrase
encapsulates an entire school of thought so well that it becomes emblematic (for
example, Hannah Arendt’s “the banality of evil”). Chalmers’s famous
formulation accomplishes this very well.

When discussing consciousness, it becomes very easy to slip into
considering the observable and measurable attributes that we associate with
being conscious, but this approach misses the very essence of the idea. I just
mentioned the concept of metacognition—the idea of thinking about one’s own
thinking—as one such correlate of consciousness. Other observers conflate
emotional intelligence or moral intelligence with consciousness. But, again, our
ability to express a loving sentiment, to get the joke, or to be sexy are simply

types of performances—impressive and intelligent perhaps, but skills that can
nonetheless be observed and measured (even if we argue about how to assess
them). Figuring out how the brain accomplishes these sorts of tasks and what is
going on in the brain when we do them constitutes Chalmers’s “easy” question
of consciousness. Of course, the “easy” problem is anything but and represents
perhaps the most difficult and important scientific quest of our era. Chalmers’s
“hard” question, meanwhile, is so hard that it is essentially ineffable.

In support of this distinction, Chalmers introduces a thought experiment
involving what he calls zombies. A zombie is an entity that acts just like a person
but simply does not have subjective experience—that is, a zombie is not
conscious. Chalmers argues that since we can conceive of zombies, they are at
least logically possible. If you were at a cocktail party and there were both
“normal” humans and zombies, how would you tell the difference? Perhaps this
sounds like a cocktail party you have attended.

Many people answer this question by saying they would interrogate
individuals they wished to assess about their emotional reactions to events and
ideas. A zombie, they believe, would betray its lack of subjective experience
through a deficiency in certain types of emotional responses. But an answer
along these lines simply fails to appreciate the assumptions of the thought
experiment. If we encountered an unemotional person (such as an individual
with certain emotional deficits, as is common in certain types of autism) or an
avatar or a robot that was not convincing as an emotional human being, then that
entity is not a zombie. Remember: According to Chalmers’s assumption, a
zombie is completely normal in his ability to respond, including the ability to
react emotionally; he is just lacking subjective experience. The bottom line is
that there is no way to identify a zombie, because by definition there is no
apparent indication of his zombie nature in his behavior. So is this a distinction
without a difference?

Chalmers does not attempt to answer the hard question but does provide
some possibilities. One is a form of dualism in which consciousness per se does
not exist in the physical world but rather as a separate ontological reality.
According to this formulation, what a person does is based on the processes in
her brain. Because the brain is causally closed, we can fully explain a person’s
actions, including her thoughts, through its processes. Consciousness then exists
essentially in another realm, or at least is a property separate from the physical
world. This explanation does not permit the mind (that is to say, the conscious
property associated with the brain) to causally affect the brain.

Another possibility that Chalmers entertains, which is not logically distinct
from his notion of dualism, and is often called panprotopsychism, holds that all

physical systems are conscious, albeit a human is more conscious than, say, a
light switch. I would certainly agree that a human brain has more to be conscious
about than a light switch.

My own view, which is perhaps a subschool of panprotopsychism, is that
consciousness is an emergent property of a complex physical system. In this
view a dog is also conscious but somewhat less than a human. An ant has some
level of consciousness, too, but much less that of a dog. The ant colony, on the
other hand, could be considered to have a higher level of consciousness than the
individual ant; it is certainly more intelligent than a lone ant. By this reckoning,
a computer that is successfully emulating the complexity of a human brain
would also have the same emergent consciousness as a human.

Another way to conceptualize the concept of consciousness is as a system
that has “qualia.” So what are qualia? One definition of the term is “conscious
experiences.” That, however, does not take us very far. Consider this thought
experiment: A neuroscientist is completely color-blind—not the sort of color¬
blind in which one mixes up certain shades of, say, green and red (as I do), but
rather a condition in which the afflicted individual lives entirely in a black-and-
white world. (In a more extreme version of this scenario, she has grown up in a
black-and-white world and has never seen any colors. Bottom line, there is no
color in her world.) However, she has extensively studied the physics of color—
she is aware that the wavelength of red light is 700 nanometers—as well as the
neurological processes of a person who can experience colors normally, and thus
knows a great deal about how the brain processes color. She knows more about
color than most people. If you wanted to help her out and explain what this
actual experience of “red” is like, how would you do it?

Perhaps you would read her a section from the poem “Red” by the Nigerian
poet Oluseyi Oluseun:

Red the colour of blood
the symbol of life
Red the colour of danger
the symbol of death

Red the colour of roses
the symbol of beauty
Red the colour of lovers
the symbol of unity

Red the colour of tomato
the symbol of good health
Red the colour of hot fire
the symbol of burning desire

That actually would give her a pretty good idea of some of the associations
people have made with red, and may even enable her to hold her own in a
conversation about the color. (“Yes, I love the color red, it’s so hot and fiery, so
dangerously beautiful...”) If she wanted to, she could probably convince people
that she had experienced red, but all the poetry in the world would not actually
enable her to have that experience.

Similarly, how would you explain what it feels like to dive into water to
someone who has never touched water? We would again be forced to resort to
poetry, but there is really no way to impart the experience itself. These
experiences are what we refer to as qualia.

Many of the readers of this book have experienced the color red. But how
do I know whether your experience of red is not the same experience that I have
when I look at blue? We both look at a red object and state assuredly that it is
red, but that does not answer the question. I may be experiencing what you
experience when you look at blue, but we have both learned to call red things
red. We could start swapping poems again, but they would simply reflect the
associations that people have made with colors; they do not speak to the actual
nature of the qualia. Indeed, congenitally blind people have read a great deal
about colors, as such references are replete in literature, and thus they do have
some version of an experience of color. How does their experience of red
compare with the experience of sighted people? This is really the same question
as the one concerning the woman in the black-and-white world. It is remarkable
that such common phenomena in our lives are so completely ineffable as to
make a simple confirmation, like one that we are experiencing the same qualia,
impossible.

Another definition of qualia is the feeling of an experience. However, this
definition is no less circular than our attempts at defining consciousness above,
as the phrases “feeling,” “having an experience,” and “consciousness” are all
synonyms. Consciousness and the closely related question of qualia are a
fundamental, perhaps the ultimate, philosophical question (although the issue of
identity may be even more important, as I will discuss in the closing section of
this chapter).

So with regard to consciousness, what exactly is the question again? It is
this: Who or what is conscious? I refer to “mind” in the title of this book rather

than “brain” because a mind is a brain that is conscious. We could also say that a
mind has free will and identity. The assertion that these issues are philosophical
is itself not self-evident. I maintain that these questions can never be fully
resolved through science. In other words, there are no falsifiable experiments
that we can contemplate that would resolve them, not without making
philosophical assumptions. If we were building a consciousness detector, Searle
would want it to ascertain that it was squirting biological neurotransmitters.
American philosopher Daniel Dennett (born in 1942) would be more flexible on
substrate, but might want to determine whether or not the system contained a
model of itself and of its own performance. That view comes closer to my own,
but at its core is still a philosophical assumption.

Proposals have been regularly presented that purport to be scientific
theories linking consciousness to some measurable physical attribute—what
Searle refers to as the “mechanism for causing consciousness.” American
scientist, philosopher, and anesthesiologist Stuart Hameroff (born in 1947) has
written that “cytoskeletal filaments are the roots of consciousness.”- He is
referring to thin threads in every cell (including neurons but not limited to them)
called microtubules, which give each cell structural integrity and play a role in
cell division. His books and papers on this issue contain detailed descriptions
and equations that explain the plausibility that the microtubules play a role in
information processing within the cell. But the connection of microtubules to
consciousness requires a leap of faith not fundamentally different from the leap
of faith implicit in a religious doctrine that describes a supreme being bestowing
consciousness (sometimes referred to as a “soul”) to certain (usually human)
entities. Some weak evidence is proffered for Hameroff’s view, specifically the
observation that the neurological processes that could support this purported
cellular computing are stopped during anesthesia. But this is far from compelling
substantiation, given that lots of processes are halted during anesthesia. We
cannot even say for certain that subjects are not conscious when anesthetized.
All we do know is that people do not remember their experiences afterward.
Even that is not universal, as some people do remember—accurately—their
experience while under anesthesia, including, for example, conversations by
their surgeons. Called anesthesia awareness, this phenomenon is estimated to
occur about 40,000 times a year in the United States. 2 But even setting that
aside, consciousness and memory are completely different concepts. As I have
discussed extensively, if I think back on my moment-to-moment experiences
over the past day, I have had a vast number of sensory impressions yet I
remember very few of them. Was I therefore not conscious of what I was seeing

and hearing all day? It is actually a good question, and the answer is not so clear.

English physicist and mathematician Roger Penrose (born in 1931) took a
different leap of faith in proposing the source of consciousness, though his also
concerned the microtubules—specifically, their purported quantum computing
abilities. His reasoning, although not explicitly stated, seemed to be that
consciousness is mysterious, and a quantum event is also mysterious, so they
must be linked in some way.

Penrose started his analysis with Turing’s theorems on unsolvable problems
and Godel’s related incompleteness theorem. Turing’s premise (which was
discussed in greater detail in chapter 8 ) is that there are algorithmic problems
that can be stated but that cannot be solved by a Turing machine. Given the
computational universality of the Turing machine, we can conclude that these
“unsolvable problems” cannot be solved by any machine. Godel’s
incompleteness theorem has a similar result with regard to the ability to prove
conjectures involving numbers. Penrose’s argument is that the human brain is
able to solve these unsolvable problems, so is therefore capable of doing things
that a deterministic machine such as a computer is unable to do. His motivation,
at least in part, is to elevate human beings above machines. But his central
premise—that humans can solve Turing’s and Godel’s insoluble problems—is
unfortunately simply not true.

A famous unsolvable problem called the busy beaver problem is stated as
follows: Find the maximum number of Is that a Turing machine with a certain
number of states can write on its tape. So to determine the busy beaver of the
number n, we build all of the Turing machines that have n states (which will be a
finite number if n is finite) and then determine the largest number of Is that these
machines write on their tapes, excluding those Turing machines that get into an
infinite loop. This is unsolvable because as we seek to simulate all of these n-
state Turing machines, our simulator will get into an infinite loop when it
attempts to simulate one of the Turing machines that does get into an infinite
loop. However, it turns out that computers have nonetheless been able to
determine the busy beaver function for certain ns. So have humans, but
computers have solved the problem for far more ns than unassisted humans.
Computers are generally better than humans at solving Turing’s and Godel’s
unsolvable problems.

Penrose linked these claimed transcendent capabilities of the human brain
to the quantum computing that he hypothesized took place in it. According to
Penrose, these neural quantum effects were somehow inherently not achievable
by computers, so therefore human thinking has an inherent edge. In fact,
common electronics uses quantum effects (transistors rely on quantum tunneling

of electrons across barriers); quantum computing in the brain has never been
demonstrated; human mental performance can be satisfactorily explained by
classical computing methods; and in any event nothing bars us from applying
quantum computing in computers. None of these objections has ever been
addressed by Penrose. It was when critics pointed out that the brain is a warm
and messy place for quantum computing that Hameroff and Penrose joined
forces. Penrose found a perfect vehicle within neurons that could conceivably
support quantum computing—namely, the microtubules that Hameroff had
speculated were part of the information processing within a neuron. So the
Hameroff-Penrose thesis is that the microtubules in the neurons are doing
quantum computing and that this is responsible for consciousness.

This thesis has also been criticized, for example, by Swedish American
physicist and cosmologist Max Tegmark (born in 1967), who determined that
quantum events in microtubules could survive for only 10“ 13 seconds, which is
much too brief a period of time either to compute results of any significance or
to affect neural processes. There are certain types of problems for which
quantum computing would show superior capabilities to classical computing—
for example, the cracking of encryption codes through the factoring of large
numbers. However, unassisted human thinking has proven to be terrible at
solving them, and cannot match even classical computers in this area, which
suggests that the brain is not demonstrating any quantum computing capabilities.
Moreover, even if such a phenomenon as quantum computing in the brain did
exist, it would not necessarily be linked to consciousness.

You Gotta Have Faith

What a piece of work is a man! How noble in reason! How infinite in
faculties! In form and moving, how express and admirable! In action how
like an angel! In apprehension, how like a god! The beauty of the world!
The paragon of animals! And yet, to me, what is this quintessence of dust?

—Hamlet, in Shakespeare’s Hamlet

The reality is that these theories are all leaps of faith, and I would add that where
consciousness is concerned, the guiding principle is “you gotta have faith”—that
is, we each need a leap of faith as to what and who is conscious, and who and
what we are as conscious beings. Otherwise we could not get up in the morning.
But we should be honest about the fundamental need for a leap of faith in this
matter and self-reflective as to what our own particular leap involves.

People have very different leaps, despite impressions to the contrary.
Individual philosophical assumptions about the nature and source of
consciousness underlie disagreements on issues ranging from animal rights to
abortion, and will result in even more contentious future conflicts over machine
rights. My objective prediction is that machines in the future will appear to be
conscious and that they will be convincing to biological people when they speak
of their qualia. They will exhibit the full range of subtle, familiar emotional
cues; they will make us laugh and cry; and they will get mad at us if we say that
we don’t believe that they are conscious. (They will be very smart, so we won’t
want that to happen.) We will come to accept that they are conscious persons.
My own leap of faith is this: Once machines do succeed in being convincing
when they speak of their qualia and conscious experiences, they will indeed
constitute conscious persons. I have come to my position via this thought
experiment: Imagine that you meet an entity in the future (a robot or an avatar)
that is completely convincing in her emotional reactions. She laughs
convincingly at your jokes, and in turn makes you laugh and cry (but not just by
pinching you). She convinces you of her sincerity when she speaks of her fears
and longings. In every way, she seems conscious. She seems, in fact, like a
person. Would you accept her as a conscious person?

If your initial reaction is that you would likely detect some way in which

she betrays her nonbiological nature, then you are not keeping to the
assumptions in this hypothetical situation, which established that she is fully
convincing. Given that assumption, if she were threatened with destruction and
responded, as a human would, with terror, would you react in the same
empathetic way that you would if you witnessed such a scene involving a
human? For myself, the answer is yes, and I believe the answer would be the
same for most if not virtually all other people regardless of what they might
assert now in a philosophical debate. Again, the emphasis here is on the word
“convincing.”

There is certainly disagreement on when or even whether we will encounter
such a nonbiological entity. My own consistent prediction is that this will first
take place in 2029 and become routine in the 2030s. But putting the time frame
aside, I believe that we will eventually come to regard such entities as conscious.
Consider how we already treat them when we are exposed to them as characters
in stories and movies: R2D2 from the Star Wars movies, David and Teddy from
the movie A.I., Data from the TV series Star Trek: The Next Generation, Johnny
5 from the movie Short Circuit, WALL-E from Disney’s movie Wall-E, T-800—
the (good) Terminator—in the second and later Terminator movies, Rachael the
Replicant from the movie Blade Runner (who, by the way, is not aware that she
is not human), Bumblebee from the movie, TV, and comic series Transformers,
and Sonny from the movie I, Robot. We do empathize with these characters even
though we know that they are nonbiological. We regard them as conscious
persons, just as we do biological human characters. We share their feelings and
fear for them when they get into trouble. If that is how we treat fictional
nonbiological characters today, then that is how we will treat real-life
intelligences in the future that don’t happen to have a biological substrate.

If you do accept the leap of faith that a nonbiological entity that is
convincing in its reactions to qualia is actually conscious, then consider what
that implies: namely that consciousness is an emergent property of the overall
pattern of an entity, not the substrate it runs on.

There is a conceptual gap between science, which stands for objective
measurement and the conclusions we can draw thereby, and consciousness,
which is a synonym for subjective experience. We obviously cannot simply ask
an entity in question, “Are you conscious?” If we look inside its “head,”
biological or otherwise, to ascertain that, then we would have to make
philosophical assumptions in determining what it is that we are looking for. The
question as to whether or not an entity is conscious is therefore not a scientific
one. Based on this, some observers go on to question whether consciousness
itself has any basis in reality. English writer and philosopher Susan Blackmore

(bom in 1951) speaks of the “grand illusion of consciousness.” She
acknowledges the reality of the meme (idea) of consciousness—in other words,
consciousness certainly exists as an idea, and there are a great many neocortical
structures that deal with the idea, not to mention words that have been spoken
and written about it. But it is not clear that it refers to something real. Blackburn
goes on to explain that she is not necessarily denying the reality of
consciousness, but rather attempting to articulate the sorts of dilemmas we
encounter when we try to pin down the concept. As British psychologist and
writer Stuart Sutherland (1927-1998) wrote in the International Dictionary of
Psychology, “Consciousness is a fascinating but elusive phenomenon; it is
impossible to specify what it is, what it does, or why it evolved.”-

However, we would be well advised not to dismiss the concept too easily as
just a polite debate between philosophers—which, incidentally, dates back two
thousand years to the Platonic dialogues. The idea of consciousness underlies
our moral system, and our legal system in turn is loosely built on those moral
beliefs. If a person extinguishes someone’s consciousness, as in the act of
murder, we consider that to be immoral, and with some exceptions, a high crime.
Those exceptions are also relevant to consciousness, in that we might authorize
police or military forces to kill certain conscious people to protect a greater
number of other conscious people. We can debate the merits of particular
exceptions, but the underlying principle holds true.

Assaulting someone and causing her to experience suffering is also
generally considered immoral and illegal. If I destroy my property, it is probably
acceptable. If I destroy your property without your permission, it is probably not
acceptable, but not because I am causing suffering to your property, but rather to
you as the owner of the property. On the other hand, if my property includes a
conscious being such as an animal, then I as the owner of that animal do not
necessarily have free moral or legal rein to do with it as I wish—there are, for
example, laws against animal cruelty.

Because a great deal of our moral and legal system is based on protecting
the existence of and preventing the unnecessary suffering of conscious entities,
in order to make responsible judgments we need to answer the question as to
who is conscious. That question is therefore not simply a matter for intellectual
debate, as is evident in the controversy surrounding an issue like abortion. I
should point out that the abortion issue can go somewhat beyond the issue of
consciousness, as pro-life proponents argue that the potential for an embryo to
ultimately become a conscious person is sufficient reason for it to be awarded
protection, just as someone in a coma deserves that right. But fundamentally the
issue is a debate about when a fetus becomes conscious.

Perceptions of consciousness also often affect our judgments in
controversial areas. Looking at the abortion issue again, many people make a
distinction between a measure like the morning-after pill, which prevents the
implantation of an embryo in the uterus in the first days of pregnancy, and a late-
stage abortion. The difference has to do with the likelihood that the late-stage
fetus is conscious. It is difficult to maintain that a few-days-old embryo is
conscious unless one takes a panprotopsychist position, but even in these terms it
would rank below the simplest animal in terms of consciousness. Similarly, we
have very different reactions to the maltreatment of great apes versus, say,
insects. No one worries too much today about causing pain and suffering to our
computer software (although we do comment extensively on the ability of
software to cause us suffering), but when future software has the intellectual,
emotional, and moral intelligence of biological humans, this will become a
genuine concern.

Thus my position is that I will accept nonbiological entities that are fully
convincing in their emotional reactions to be conscious persons, and my
prediction is that the consensus in society will accept them as well. Note that this
definition extends beyond entities that can pass the Turing test, which requires
mastery of human language. The latter are sufficiently humanlike that I would
include them, and I believe that most of society will as well, but I also include
entities that evidence humanlike emotional reactions but may not be able to pass
the Turing test—for example, young children.

Does this resolve the philosophical question of who is conscious, at least
for myself and others who accept this particular leap of faith? The answer is: not
quite. We’ve only covered one case, which is that of entities that act in a
humanlike way. Even though we are discussing future entities that are not
biological, we are talking about entities that demonstrate convincing humanlike
reactions, so this position is still human-centric. But what about more alien
forms of intelligence that are not humanlike? We can imagine intelligences that
are as complex as or perhaps vastly more complex and intricate than human
brains, but that have completely different emotions and motivations. How do we
decide whether or not they are conscious?

We can start by considering creatures in the biological world that have
brains comparable to those of humans yet evince very different sorts of
behaviors. British philosopher David Cockburn (born in 1949) writes about
viewing a video of a giant squid that was under attack (or at least it thought it
was—Cockburn hypothesized that it might have been afraid of the human with
the video camera). The squid shuddered and cowered, and Cockburn writes, “It
responded in a way which struck me immediately and powerfully as one of fear.

Part of what was striking in this sequence was the way in which it was possible
to see in the behavior of a creature physically so very different from human
beings an emotion which was so unambiguously and specifically one of fear.”-
He concludes that the animal was feeling that emotion and he articulates the
belief that most other people viewing that film would come to the same
conclusion. If we accept Cockburn’s description and conclusion, then we would
have to add giant squids to our list of conscious entities. However, this has not
gotten us very far either, because it is still based on our empathetic reaction to an
emotion that we recognize in ourselves. It is still a self-centric or human-centric
perspective.

If we step outside biology, nonbiological intelligence will be even more
varied than intelligence in the biological world. For example, some entities may
not have a fear of their own destruction, and may not have a need for the
emotions we see in humans or in any biological creature. Perhaps they could still
pass the Turing test, or perhaps they wouldn’t even be willing to try.

We do in fact build robots today that do not have a sense of self-
preservation to carry out missions in dangerous environments. They’re not
sufficiently intelligent or complex yet for us to seriously consider their sentience,
but we can imagine future robots of this sort that are as complex as humans.
What about them?

Personally I would say that if I saw in such a device’s behavior a
commitment to a complex and worthy goal and the ability to execute notable
decisions and actions to carry out its mission, I would be impressed and probably
become upset if it got destroyed. This is now perhaps stretching the concept a
bit, in that I am responding to behavior that does not include many emotions we
consider universal in people and even in biological creatures of all kinds. But
again, I am seeking to connect with attributes that I can relate to in myself and
other people. The idea of an entity totally dedicated to a noble goal and carrying
it out or at least attempting to do so without regard for its own well-being is,
after all, not completely foreign to human experience. In this instance we are
also considering an entity that is seeking to protect biological humans or in some
way advance our agenda.

What if this entity has its own goals distinct from a human one and is not
carrying out a mission we would recognize as noble in our own terms? I might
then attempt to see if I could connect or appreciate some of its abilities in some
other way. If it is indeed very intelligent, it is likely to be good at math, so
perhaps I could have a conversation with it on that topic. Maybe it would
appreciate math jokes.

But if the entity has no interest in communicating with me, and I don’t have

sufficient access to its actions and decision making to be moved by the beauty of
its internal processes, does that mean that it is not conscious? I need to conclude
that entities that do not succeed in convincing me of their emotional reactions, or
that don’t care to try, are not necessarily not conscious. It would be difficult to
recognize another conscious entity without establishing some level of empathetic
communication, but that judgment reflects my own limitations more than it does
the entity under consideration. We thus need to proceed with humility. It is
challenging enough to put ourselves in the subjective shoes of another human, so
the task will be that much harder with intelligences that are extremely different
from our own.

What Are We Conscious Of?

If we could look through the skull into the brain of a consciously thinking
person, and if the place of optimal excitability were luminous, then we
should see playing over the cerebral surface, a bright spot with fantastic,
waving borders constantly fluctuating in size and form, surrounded by a
darkness more or less deep, covering the rest of the hemisphere.

—Ivan Petrovich Pavlov, 1913-

Returning to the giant squid, we can recognize some of its apparent emotions,
but much of its behavior is a mystery. What is it like being a giant squid? How
does it feel as it squeezes its spineless body through a tiny opening? We don’t
even have the vocabulary to answer this question, given that we cannot even
describe experiences that we do share with other people, such as seeing the color
red or feeling water splash on our bodies.

But we don’t have to go as far as the bottom of the ocean to find mysteries
in the nature of conscious experiences—we need only consider our own. I know,
for example, that I am conscious. I assume that you, the reader, are conscious
also. (As for people who have not bought my book, I am not so sure.) But what
am I conscious of? You might ask yourself the same question.

Try this thought experiment (which will work for those of you who drive a
car): Imagine that you are driving in the left lane of a highway. Now close your
eyes, grab an imagined steering wheel, and make the movements to change lanes
to the lane to your right.

Okay, before continuing to read, try it.

Here is what you probably did: You held the steering wheel. You checked
that the right lane is clear. Assuming the lane was clear, you turned the steering
wheel to the right for a brief period. Then you straightened it out again. Job
done.

It’s a good thing you weren’t in a real car, because you just zoomed across
all the lanes of the highway and crashed into a tree. While I probably should
have mentioned that you shouldn’t try this in a real moving car (but then I
assume you have already mastered the rule that you shouldn’t drive with your
eyes closed), that’s not really the key problem here. If you used the procedure I

just described—and almost everyone does when doing this thought experiment
—you got it wrong. Turning the wheel to the right and then straightening it out
causes the car to head in a direction that is diagonal to its original direction. It
will cross the lane to the right, as you intended, but it will keep going to the right
indefinitely until it zooms off the road. What you needed to do as your car
crossed the lane to the right was to then turn the wheel to the left, just as far as
you had turned it to the right, and then straighten it out again. This will cause the
car to again head straight in the new lane.

Consider the fact that if you’re a regular driver, you’ve done this maneuver
thousands of times. Are you not conscious when you do this? Have you never
paid attention to what you are actually doing when you change lanes? Assuming
that you are not reading this book in a hospital while recovering from a lane¬
changing accident, you have clearly mastered this skill. Yet you are not
conscious of what you did, however many times you’ve accomplished this task.

When people tell stories of their experiences, they describe them as
sequences of situations and decisions. But this is not how we experience a story
in the first place. Our original experience is as a sequence of high-level patterns,
some of which may have triggered feelings. We remember only a small subset of
those patterns, if that. Even if we are reasonably accurate in our recounting of a
story, we use our powers of confabulation to fill in missing details and convert
the sequence into a coherent tale. We cannot be certain what our original
conscious experience was from our recollection of it, yet memory is the only
access we have to that experience. The present moment is, well, fleeting, and is
quickly turned into a memory, or, more often, not. Even if an experience is
turned into a memory, it is stored, as the PRTM indicates, as a high-level pattern
composed of other patterns in a huge hierarchy. As I have pointed out several
times, almost ah of the experiences we have (like any of the times we changed
lanes) are immediately forgotten. So ascertaining what constitutes our own
conscious experience is actually not attainable.

East Is East and West Is West

Before brains there was no color or sound in the universe, nor was there any
flavor or aroma and probably little sense and no feeling or emotion.

—Roger W. Sperry

Rene Descartes walks into a restaurant and sits down for dinner. The waiter
comes over and asks if he’d like an appetizer.

“No thank you,” says Descartes, “I’d just like to order dinner.”

“Would you like to hear our daily specials?” asks the waiter.

“No,” says Descartes, getting impatient.

“Would you like a drink before dinner?” the waiter asks.

Descartes is insulted, since he’s a teetotaler. “I think not!” he says
indignantly, and POOF! he disappears.

—A joke as recalled by David Chalmers

There are two ways to view the questions we have been considering—converse
Western and Eastern perspectives on the nature of consciousness and of reality.
In the Western perspective, we start with a physical world that evolves patterns
of information. After a few billion years of evolution, the entities in that world
have evolved sufficiently to become conscious beings. In the Eastern view,
consciousness is the fundamental reality; the physical world only comes into
existence through the thoughts of conscious beings. The physical world, in other
words, is the thoughts of conscious beings made manifest. These are of course
simplifications of complex and diverse philosophies, but they represent the
principal polarities in the philosophies of consciousness and its relationship to
the physical world.

The East-West divide on the issue of consciousness has also found
expression in opposing schools of thought in the field of subatomic physics. In
quantum mechanics, particles exist as what are called probability fields. Any
measurement carried out on them by a measuring device causes what is called a
collapse of the wave function, meaning that the particle suddenly assumes a
particular location. A popular view is that such a measurement constitutes
observation by a conscious observer, because otherwise measurement would be a

meaningless concept. Thus the particle assumes a particular location (as well as
other properties, such as velocity) only when it is observed. Basically particles
figure that if no one is bothering to look at them, they don’t need to decide where
they are. I call this the Buddhist school of quantum mechanics, because in it
particles essentially don’t exist until they are observed by a conscious person.

There is another interpretation of quantum mechanics that avoids such
anthropomorphic terminology. In this analysis, the field representing a particle is
not a probability field, but rather just a function that has different values in
different locations. The field, therefore, is fundamentally what the particle is.
There are constraints on what the values of the field can be in different locations,
because the entire field representing a particle represents only a limited amount
of information. That is where the word “quantum” comes from. The so-called
collapse of the wave function, this view holds, is not a collapse at all. The wave
function actually never goes away. It is just that a measurement device is also
made up of particles with fields, and the interaction of the particle field being
measured and the particle fields of the measuring device results in a reading of
the particle being in a particular location. The field, however, is still present.
This is the Western interpretation of quantum mechanics, although it is
interesting to note that the more popular view among physicists worldwide is
what I have called the Eastern interpretation.

There was one philosopher whose work spanned this East-West divide. The
Austrian British thinker Ludwig Wittgenstein (1889-1951) studied the
philosophy of language and knowledge and contemplated the question of what it
is that we can really know. He pondered this subject while a soldier in World
War I and took notes for what would be his only book published while he was
alive, Tractatus Logico-Philosophicus. The work had an unusual structure, and it
was only through the efforts of his former instructor, British mathematician and
philosopher Bertrand Russell, that it found a publisher in 1921. It became the
bible for a major school of philosophy known as logical positivism, which
sought to define the limits of science. The book and the movement surrounding
it were influential on Turing and the emergence of the theory of computation and
linguistics.

Tractatus Logico-Philosophicus anticipates the insight that all knowledge is
inherently hierarchical. The book itself is arranged in nested and numbered
statements. For example, the first four statements in the book are:

1 The world is all that is the case.

1.1 The world is the totality of facts, not of things.

1.11 The world is determined by the facts, and by their being all the

facts.

1.12 For the totality of facts determines what is the case, and also
whatever is not the case.

Another significant statement in the Tractatus —and one that Turing would echo
—is this:

4.0031 All philosophy is a critique of language.

Essentially both Tractatus Logico-Philosophicus and the logical positivism
movement assert that physical reality exists separate from our perception of it,
but that all we can know of that reality is what we perceive with our senses—
which can be heightened through our tools—and the logical inferences we can
make from these sensory impressions. Essentially Wittgenstein is attempting to
describe the methods and goals of science. The final statement in the book is
number 7, “What we cannot speak about we must pass over in silence.” The
early Wittgenstein, accordingly, considers the discussion of consciousness as
circular and tautological and therefore a waste of time.

The later Wittgenstein, however, completely rejected this approach and
spent all of his philosophical attention talking about matters that he had earlier
argued should be passed over in silence. His writings on this revised thinking
were collected and published in 1953, two years after his death, in a book called
Philosophical Investigations. He criticized his earlier ideas in the Tractatus,
judging them to be circular and void of meaning, and came to the view that what
he had advised that we not speak about was in fact all that was worth reflecting
on. These writings heavily influenced the existentialists, making Wittgenstein
the only figure in modern philosophy to be a major architect of two leading and
contradictory schools of thought in philosophy.

What is it that the later Wittgenstein thought was worth thinking and talking
about? It was issues such as beauty and love, which he recognized exist
imperfectly as ideas in the minds of men. However, he writes that such concepts
do exist in a perfect and idealized realm, similar to the perfect “forms” that Plato
wrote about in the Platonic dialogues, another work that illuminated apparently
contradictory approaches to the nature of reality.

One thinker whose position I believe is mischaracterized is the French
philosopher and mathematician Rene Descartes. His famous “I think, therefore I
am” is generally interpreted to extol rational thought, in the sense that “I think,
that is I can perform logical thought, therefore I am worthwhile.” Descartes is
therefore considered the architect of the Western rational perspective.

Reading this statement in the context of his other writings, however, I get a
different impression. Descartes was troubled by what is referred to as the “mind-
body problem”: Namely, how does a conscious mind arise from the physical
matter of the brain? From this perspective, it seems he was attempting to push
rational skepticism to the breaking point, so in my view what his statement really
means is, “I think, that is to say, a subjective experience is occurring, so
therefore all we know for sure is that something—call it I —exists.” He could not
be certain that the physical world exists, because all we have are our own
individual sense impressions of it, which might be wrong or completely illusory.
We do know, however, that the experiencer exists.

My religious upbringing was in a Unitarian church, where we studied all of
the world’s religions. We would spend six months on, say, Buddhism and would
go to Buddhist services, read their books, and have discussion groups with their
leaders. Then we would switch to another religion, such as Judaism. The
overriding theme was “many paths to the truth,” along with tolerance and
transcendence. This last idea meant that resolving apparent contradictions
between traditions does not require deciding that one is right and the other is
wrong. The truth can be discovered only by finding an explanation that overrides
—transcends—seeming differences, especially for fundamental questions of
meaning and purpose.

This is how I resolve the Western-Eastern divide on consciousness and the
physical world. In my view, both perspectives have to be true.

On the one hand, it is foolish to deny the physical world. Even if we do live
in a simulation, as speculated by Swedish philosopher Nick Bostrom, reality is
nonetheless a conceptual level that is real for us. If we accept the existence of the
physical world and the evolution that has taken place in it, then we can see that
conscious entities have evolved from it.

On the other hand, the Eastern perspective—that consciousness is
fundamental and represents the only reality that is truly important—is also
difficult to deny. Just consider the precious regard we give to conscious persons
versus unconscious things. We consider the latter to have no intrinsic value
except to the extent that they can influence the subjective experience of
conscious persons. Even if we regard consciousness as an emergent property of a
complex system, we cannot take the position that it is just another attribute
(along with “digestion” and “lactation,” to quote John Searle). It represents what
is truly important.

The word “spiritual” is often used to denote the things that are of ultimate
significance. Many people don’t like to use such terminology from spiritual or
religious traditions, because it implies sets of beliefs that they may not subscribe

to. But if we strip away the mystical complexities of religious traditions and
simply respect “spiritual” as implying something of profound meaning to
humans, then the concept of consciousness fits the bill. It reflects the ultimate
spiritual value. Indeed, “spirit” itself is often used to denote consciousness.

Evolution can then be viewed as a spiritual process in that it creates
spiritual beings, that is, entities that are conscious. Evolution also moves toward
greater complexity, greater knowledge, greater intelligence, greater beauty,
greater creativity, and the ability to express more transcendent emotions, such as
love. These are all descriptions that people have used for the concept of God,
albeit God is described as having no limitations in these regards.

People often feel threatened by discussions that imply the possibility that a
machine could be conscious, as they view considerations along these lines as a
denigration of the spiritual value of conscious persons. But this reaction reflects
a misunderstanding of the concept of a machine. Such critics are addressing the
issue based on the machines they know today, and as impressive as they are
becoming, I agree that contemporary examples of technology are not yet worthy
of our respect as conscious beings. My prediction is that they will become
indistinguishable from biological humans, whom we do regard as conscious
beings, and will therefore share in the spiritual value we ascribe to
consciousness. This is not a disparagement of people; rather, it is an elevation of
our understanding of (some) future machines. We should probably adopt a
different terminology for these entities, as they will be a different sort of
machine.

Indeed, as we now look inside the brain and decode its mechanisms we
discover methods and algorithms that we can not only understand but re-create
—“the parts of a mill pushing on each other,” to paraphrase German
mathematician and philosopher Gottfried Wilhelm Leibniz (1646-1716) when he
wrote about the brain. Humans already constitute spiritual machines. Moreover,
we will merge with the tools we are creating so closely that the distinction
between human and machine will blur until the difference disappears. That
process is already well under way, even if most of the machines that extend us
are not yet inside our bodies and brains.

Free Will

A central aspect of consciousness is the ability to look ahead, the capability
we call “foresight.” It is the ability to plan, and in social terms to outline a
scenario of what is likely going to happen, or what might happen, in social
interactions that have not yet taken place.... It is a system whereby we
improve our chances of doing those things that will represent our own best
interests.... I suggest that “free will” is our apparent ability to choose and
act upon whichever of those seem most useful or appropriate, and our
insistence upon the idea that such choices are our own.

—Richard D. Alexander

Shall we say that the plant does not know what it is doing merely because it
has no eyes, or ears, or brains? If we say that it acts mechanically, and
mechanically only, shall we not be forced to admit that sundry other and
apparently very deliberate actions are also mechanical? If it seems to us that
the plant kills and eats a fly mechanically, may it not seem to the plant that
a man must kill and eat a sheep mechanically?

—Samuel Butler, 1871

Is the brain, which is notably double in structure, a double organ, “seeming
parted, but yet a union in partition”?

—Henry Maudsley-

Redundancy, as we have learned, is a key strategy deployed by the neocortex.
But there is another level of redundancy in the brain, in that its left and right
hemispheres, while not identical, are largely the same. Just as certain regions of
the neocortex normally end up processing certain types of information, the
hemispheres also specialize to some extent—for example, the left hemisphere
typically is responsible for verbal language. But these assignments can also be
rerouted, to the point that we can survive and function somewhat normally with
only one half. American neuropsychology researchers Stella de Bode and Susan
Curtiss reported on forty-nine children who had undergone a hemispherectomy

(removal of half of their brain), an extreme operation that is performed on
patients with a life-threatening seizure disorder that exists in only one
hemisphere. Some who undergo the procedure are left with deficits, but those
deficits are specific and the patients have reasonably normal personalities. Many
of them thrive, and it is not apparent to observers that they only have half a
brain. De Bode and Curtiss write about left-hemispherectomized children who
“develop remarkably good language despite removal of the ‘language’
hemisphere.”- They describe one such student who completed college, attended
graduate school, and scored above average on IQ tests. Studies have shown
minimal long-term effects on overall cognition, memory, personality, and sense
of humor.— In a 2007 study American researchers Shearwood McClelland and
Robert Maxwell showed similar long-term positive results in adults.—

A ten-year-old German girl who was born with only half of her brain has
also been reported to be quite normal. She even has almost perfect vision in one
eye, whereas hemispherectomy patients lose part of their field of vision right
after the operation.— Scottish researcher Lars Muckli commented, “The brain
has amazing plasticity but we were quite astonished to see just how well the
single hemisphere of the brain in this girl has adapted to compensate for the
missing half.”

While these observations certainly support the idea of plasticity in the
neocortex, their more interesting implication is that we each appear to have two
brains, not one, and we can do pretty well with either. If we lose one, we do lose
the cortical patterns that are uniquely stored there, but each brain is in itself
fairly complete. So does each hemisphere have its own consciousness? There is
an argument to be made that such is the case.

Consider split-brain patients, who still have both of their brain hemispheres,
but the channel between them has been cut. The corpus callosum is a bundle of
about 250 million axons that connects the left and right cerebral hemispheres and
enables them to communicate and coordinate with each other. Just as two people
can communicate closely with each other and act as a single decision maker
while remaining separate and whole individuals, the two brain hemispheres can
function as a unit while remaining independent.

As the term implies, in split-brain patients the corpus callosum has been cut
or damaged, leaving them effectively with two functional brains without a direct
communication link between them. American psychology researcher Michael
Gazzaniga (born in 1939) has conducted extensive experiments on what each
hemisphere in split-brain patients is thinking.

The left hemisphere in a split-brain patient usually sees the right visual

field, and vice versa. Gazzaniga and his colleagues showed a split-brain patient a
picture of a chicken claw to the right visual field (which was seen by his left
hemisphere) and a snowy scene to the left visual field (which was seen by his
right hemisphere). He then showed a collection of pictures so that both
hemispheres could see them. He asked the patient to choose one of the pictures
that went well with the first picture. The patient’s left hand (controlled by his
right hemisphere) pointed to a picture of a shovel, whereas his right hand pointed
to a picture of a chicken. So far so good—the two hemispheres were acting
independently and sensibly. “Why did you choose that?” Gazzaniga asked the
patient, who answered verbally (controlled by his left-hemisphere speech
center), “The chicken claw obviously goes with the chicken.” But then the
patient looked down and, noticing his left hand pointing to the shovel,
immediately explained this (again with his left-hemisphere-controlled speech
center) as “and you need a shovel to clean out the chicken shed.”

This is a confabulation. The right hemisphere (which controls the left arm
and hand) correctly points to the shovel, but because the left hemisphere (which
controls the verbal answer) is unaware of the snow, it confabulates an
explanation, yet is not aware that it is confabulating. It is taking responsibility
for an action it had never decided on and never took, but thinks that it did.

This implies that each of the two hemispheres in a split-brain patient has its
own consciousness. The hemispheres appear not to be aware that their body is
effectively controlled by two brains, because they learn to coordinate with each
other, and their decisions are sufficiently aligned and consistent that each thinks
that the decisions of the other are its own.

Gazzaniga’s experiment doesn’t prove that a normal individual with a
functioning corpus callosum has two conscious half-brains, but it is suggestive
of that possibility. While the corpus callosum allows for effective collaboration
between the two half-brains, it doesn’t necessarily mean that they are not
separate minds. Each one could be fooled into thinking it has made all the
decisions, because they would all be close enough to what each would have
decided on its own, and after all, it does have a lot of influence on each decision
(by collaborating with the other hemisphere through the corpus callosum). So to
each of the two minds it would seem as if it were in control.

How would you test the conjecture that they are both conscious? One could
assess them for neurological correlates of consciousness, which is precisely what
Gazzaniga has done. His experiments show that each hemisphere is acting as an
independent brain. Confabulation is not restricted to brain hemispheres; we each
do it on a regular basis. Each hemisphere is about as intelligent as a human, so if
we believe that a human brain is conscious, then we have to conclude that each

hemisphere is independently conscious. We can assess the neurological
correlates and we can conduct our own thought experiments (for example,
considering that if two brain hemispheres without a functioning corpus callosum
constitute two separate conscious minds, then the same would have to hold true
for two hemispheres with a functioning connection between them), but any
attempt at a more direct detection of consciousness in each hemisphere confronts
us again with the lack of a scientific test for consciousness. But if we do allow
that each hemisphere of the brain is conscious, then do we grant that the so-
called unconscious activity in the neocortex (which constitutes the vast bulk of
its activity) has an independent consciousness too? Or maybe it has more than
one? Indeed, Marvin Minsky refers to the brain as a “society of mind.”—

In another split-brain experiment the researchers showed the word “bell” to
the right brain and “music” to the left brain. The patient was asked what word he
saw. The left-hemisphere-controlled speech center says “music.” The subject
was then shown a group of pictures and asked to point to a picture most closely
related to the word he was just shown. His right-hemisphere-controlled arm
pointed to the bell. When he was asked why he pointed to the bell, his left-
hemisphere-controlled speech center replied, “Well, music, the last time I heard
any music was the bells banging outside here.” He provided this explanation
even though there were other pictures to choose from that were much more
closely related to music.

Again, this is a confabulation. The left hemisphere is explaining as if it
were its own a decision that it never made and never carried out. It is not doing
so to cover up for a friend (that is, its other hemisphere)—it genuinely thinks
that the decision was its own.

These reactions and decisions can extend to emotional responses. They
asked a teenage split-brain patient—so that both hemispheres heard—“Who is
your favorite...” and then fed the word “girlfriend” just to the right hemisphere
through the left ear. Gazzaniga reports that the subject blushed and acted
embarrassed, an appropriate reaction for a teenager when asked about his
girlfriend. But the left-hemisphere-controlled speech center reported that it had
not heard any word and asked for clarification: “My favorite what?” When asked
again to answer the question, this time in writing, the right-hemisphere-
controlled left hand wrote out his girlfriend’s name.

Gazzaniga’s tests are not thought experiments but actual mind experiments.
While they offer an interesting perspective on the issue of consciousness, they
speak even more directly to the issue of free will. In each of these cases, one of
the hemispheres believes that it has made a decision that it in fact never made.
To what extent is that true for the decisions we make every day?

Consider the case of a ten-year-old female epileptic patient. Neurosurgeon
Itzhak Fried was performing brain surgery while she was awake (which is
feasible because there are no pain receptors in the brain).— Whenever he
stimulated a particular spot on her neocortex, she would laugh. At first the
surgical team thought that they might be triggering some sort of laugh reflex, but
they quickly realized that they were triggering the actual perception of humor.
They had apparently found a point in her neocortex—there is obviously more
than one—that recognizes the perception of humor. She was not just laughing—
she actually found the situation funny, even though nothing had actually changed
in the situation other than their having stimulated this point in her neocortex.
When they asked her why she was laughing, she did not reply along the lines of,
“Oh, no particular reason,” or “You just stimulated my brain,” but would
immediately confabulate a reason. She would point to something in the room
and try to explain why it was funny. “You guys are just so funny standing there”
was a typical comment.

We are apparently very eager to explain and rationalize our actions, even
when we didn’t actually make the decisions that led to them. So just how
responsible are we for our decisions? Consider these experiments by physiology
professor Benjamin Libet (1916-2007) at the University of California at Davis.
Libet had participants sit in front of a timer, EEG electrodes attached to their
scalps. He instructed them to do simple tasks such as pushing a button or moving
a finger. The participants were asked to note the time on the timer when they
“first become aware of the wish or urge to act.” Tests indicated a margin of error
of only 50 milliseconds on these assessments by the subjects. They also
measured an average of about 200 milliseconds between the time when the
subjects reported awareness of the urge to act and the actual act.—

The researchers also looked at the EEG signals coming from the subjects’
brains. Brain activity involved in initiating the action by the motor cortex (which
is responsible for carrying out the action) actually occurred on average about 500
milliseconds prior to the performance of the task. That means that the motor
cortex was preparing to carry out the task about a third of a second before the
subject was even aware that she had made a decision to do so.

The implications of the Libet experiments have been hotly debated. Libet
himself concluded that our awareness of decision making appears to be an
illusion, that “consciousness is out of the loop.” Philosopher Daniel Dennett
commented, “The action is originally precipitated in some part of the brain, and
off fly the signals to muscles, pausing en route to tell you, the conscious agent,
what is going on (but like all good officials letting you, the bumbling president,

maintain the illusion that you started it all).”— At the same time Dennett has
questioned the timings recorded by the experiment, basically arguing that
subjects may not really be aware of when they become aware of the decision to
act. One might wonder: If the subject is unaware of when she is aware of making
a decision, then who is? But the point is actually well taken—as I discussed
earlier, what we are conscious of is far from clear.

Indian American neuroscientist Vilayanur Subramanian “Rama”
Ramachandran (born in 1951) explains the situation a little differently. Given
that we have on the order of 30 billion neurons in the neocortex, there is always
a lot going on there, and we are consciously aware of very little of it. Decisions,
big and little, are constantly being processed by the neocortex, and proposed
solutions bubble up to our conscious awareness. Rather than free will,
Ramachandran suggests we should talk about “free won’t”—that is, the power to
reject solutions proposed by the nonconscious parts of our neocortex.

Consider the analogy to a military campaign. Army officials prepare a
recommendation to the president. Prior to receiving the president’s approval,
they perform preparatory work that will enable the decision to be carried out. At
a particular moment, the proposed decision is presented to the president, who
approves it, and the rest of the mission is then undertaken. Since the “brain”
represented by this analogy involves the unconscious processes of the neocortex
(that is, the officials under the president) as well as its conscious processes (the
president), we would see neural activity as well as actual actions taking place
prior to the official decision’s being made. We can always get into debates in a
particular situation as to how much leeway the officials under the president
actually gave him or her to accept or reject a recommendation, and certainly
American presidents have done both. But it should not surprise us that mental
activity, even in the motor cortex, would start before we were aware that there
was a decision to be made.

What the Libet experiments do underscore is that there is a lot of activity in
our brains underlying our decisions that is not conscious. We already knew that
most of what goes in the neocortex is not conscious; it should not be surprising,
therefore, that our actions and decisions stem from both unconscious and
conscious activity. Is this distinction important? If our decisions arise from both,
should it matter if we sort out the conscious parts from the unconscious? Is it not
the case that both aspects represent our brain? Are we not ultimately responsible
for everything that goes on in our brains? “Yes, I shot the victim, but I’m not
responsible because I wasn’t paying attention” is probably a weak defense. Even
though there are some narrow legal grounds on which a person may not be held
responsible for his decisions, we are generally held accountable for all of the

choices we make.

The observations and experiments I have cited above constitute thought
experiments on the issue of free will, a subject that, like the topic of
consciousness, has been debated since Plato. The term “free will” itself dates
back to the thirteenth century, but what exactly does it mean?

The Merriam-Webster dictionary defines it as the “freedom of humans to
make choices that are not determined by prior causes or by divine intervention.”
You will notice that this definition is hopelessly circular: “Free will is
freedom....” Setting aside the idea of divine intervention’s standing in
opposition to free will, there is one useful element in this definition, which is the
idea of a decision’s “not [being] determined by prior causes.” I’ll come back to
that momentarily.

The Stanford Encyclopedia of Philosophy states that free will is the
“capacity of rational agents to choose a course of action from among various
alternatives.” By this definition, a simple computer is capable of free will, so it is
less helpful than the dictionary definition.

Wikipedia is actually a bit better. It defines free will as “the ability of agents
to make choices free from certain kinds of constraints.... The constraint of
dominant concern has been...determinism.” Again, it uses the circular word
“free” in defining free will, but it does articulate what has been regarded as the
principal enemy of free will: determinism. In that respect the Merriam-Webster
definition above is actually similar in its reference to decisions that “are not
determined by prior causes.”

So what do we mean by determinism? If I put “2 + 2” into a calculator and
it displays “4,” can I say that the calculator displayed its free will by deciding to
display that “4”? No one would accept that as a demonstration of free will,
because the “decision” was predetermined by the internal mechanisms of the
calculator and the input. If I put in a more complex calculation, we still come to
the same conclusion with regard to its lack of free will.

How about Watson when it answers a Jeopardy! query? Although its
deliberations are far more complex than those of the calculator, very few if any
observers would ascribe free will to its decisions. No one human knows exactly
how all of its programs work, but we can identify a group of people who
collectively can describe all of its methods. More important, its output is
determined by (1) all of its programs at the moment that the query is posed, (2)
the query itself, (3) the state of its internal parameters that influence its
decisions, and (4) its trillions of bytes of knowledge bases, including
encyclopedias. Based on these four categories of information, its output is
determined. We might speculate that presenting the same query would always

get the same response, but Watson is programmed to learn from its experience,
so there is the possibility that subsequent answers would be different. However,
that does not contradict this analysis; rather, it just constitutes a change in item 3,
the parameters that control its decisions.

So how exactly does a human differ from Watson, such that we ascribe free
will to the human but not to the computer program? We can identify several
factors. Even though Watson is a better Jeopardy! player than most if not all
humans, it is nonetheless not nearly as complex as a human neocortex. Watson
does possess a lot of knowledge, and it does use hierarchical methods, but the
complexity of its hierarchical thinking is still considerably less than that of a
human. So is the difference simply one of the scale of complexity of its
hierarchical thinking? There is an argument to be made that the issue does come
down to this. In my discussion of the issue of consciousness I noted that my own
leap of faith is that I would consider a computer that passed a valid Turing test to
be conscious. The best chatbots are not able to do that today (although they are
steadily improving), so my conclusion with regard to consciousness is a matter
of the level of performance of the entity. Perhaps the same is true of my
ascribing free will to it.

Consciousness is indeed one philosophical difference between human
brains and contemporary software programs. We consider human brains to be
conscious, whereas we do not— yet —attribute that to software programs. Is this
the factor we are looking for that underlies free will?

A simple mind experiment would argue that consciousness is indeed a vital
part of free will. Consider a situation in which someone performs an action with
no awareness that she is doing it—it is carried out entirely by nonconscious
activity in that person’s brain. Would we regard this to be a display of free will?
Most people would answer no. If the action was harmful, we would probably
still hold that person responsible but look for some recent conscious acts that
may have caused that person to perform actions without conscious awareness,
such as taking one drink too many, or just failing to train herself adequately to
consciously consider her decisions before she acted on them.

According to some commentators, the Libet experiments argued against
free will by highlighting how much of our decision making is not conscious.
Since there is a reasonable consensus among philosophers that free will does
imply conscious decision making, it appears to be one prerequisite for free will.
However, to many observers, consciousness is a necessary but not sufficient
condition. If our decisions—conscious or otherwise—are predetermined before
we make them, how can we say that our decisions are free? This position, which
holds that free will and determinism are not compatible, is known as

incompatibilism. For example, American philosopher Carl Ginet (born in 1932)
argues that if events in the past, present, and future are determined, then we can
be considered to have no control over them or their consequences. Our apparent
decisions and actions are simply part of this predetermined sequence. To Ginet,
this rules out free will.

Not everyone regards determinism as being incompatible with the concept
of free will, however. The compatibilists argue, essentially, that you’re free to
decide what you want even though what you decide is or may be determined.
Daniel Dennett, for example, argues that while the future may be determined
from the state of the present, the reality is that the world is so intricately complex
that we cannot possibly know what the future will bring. We can identify what
he refers to as “expectations,” and we are indeed free to perform acts that differ
from these expectations. We should consider how our decisions and actions
compare to these expectations, not to a theoretically determined future that we
cannot in fact know. That, Dennett argues, is sufficient for free will.

Gazzaniga also articulates a compatibilist position: “We are personally
responsible agents and are to be held accountable for our actions, even though
we live in a determined world.”- A cynic might interpret this view as: You have
no control over your actions, but we’ll blame you anyway.

Some thinkers dismiss the idea of free will as an illusion. Scottish
philosopher David Hume (1711-1776) described it as simply a “verbal” matter
characterized by “a false sensation or seeming experience.”— German
philosopher Arthur Schopenhauer (1788-1860) wrote that “everyone believes
himself a priori to be perfectly free, even in his individual actions, and thinks
that at every moment he can commence another manner of life.... But a
posteriori, through experience, he finds to his astonishment that he is not free,
but subjected to necessity, that in spite of all his resolutions and reflections he
does not change his conduct, and that from the beginning of his life to the end of
it, he must carry out the very character which he himself condemns.”—

I would add several points here. The concept of free will—and
responsibility, which is a closely aligned idea—is useful, and indeed vital, to
maintaining social order, whether or not free will actually exists. Just as
consciousness clearly exists as a meme, so too does free will. Attempts to prove
its existence, or even to define it, may become hopelessly circular, but the reality
is that almost everyone believes in the idea. Very substantial portions of our
higher-level neocortex are devoted to the concept that we make free choices and
are responsible for our actions. Whether in a strict philosophical sense that is
true or even possible, society would be far worse off if we did not have such

beliefs.

Furthermore, the world is not necessarily determined. I discussed above two
perspectives on quantum mechanics, which differ with respect to the relationship
of quantum fields to an observer. A popular interpretation of the observer-based
perspective provides a role for consciousness: Particles do not resolve their
quantum ambiguity until observed by a conscious observer. There is another split
in the philosophy of quantum events that has a bearing on our discussion of free
will, one that revolves around the question: Are quantum events determined or
random?

The most common interpretation of a quantum event is that when the wave
function constituting a particle “collapses,” the particle’s location becomes
specific. Over a great many such events, there will be a predictable distribution
(which is why the wave function is considered to be a probability distribution),
but the resolution for each such particle undergoing a collapse of its wave
function is random. The opposing interpretation is deterministic: specifically,
that there is a hidden variable that we are unable to detect separately, but whose
value determines the particle’s position. The value or phase of the hidden
variable at the moment of the wave function collapse determines the position of
the particle. Most quantum physicists seem to favor the idea of a random
resolution according to the probability field, but the equations for quantum
mechanics do allow for the existence of such a hidden variable.

Thus the world may not be determined after all. According to the
probability wave interpretation of quantum mechanics, there is a continual
source of uncertainty at the most basic level of reality. However, this observation
does not necessarily resolve the concerns of the incompatibilists. It is true that
under this interpretation of quantum mechanics, the world is not determined, but
our concept of free will extends beyond decisions and actions that are merely
random. Most incompatibilists would find the concept of free will to also be
incompatible with our decisions’ being essentially accidental. Free will seems to
imply purposeful decision making.

Dr. Wolfram proposes a way to resolve the dilemma. His book A New Kind
of Science (2002) presents a comprehensive view of the idea of cellular automata
and their role in every facet of our lives. A cellular automaton is a mechanism in
which the value of information cells is continually recomputed as a function of
the cells near it. John von Neumann created a theoretical self-replicating
machine called a universal constructor that was perhaps the first cellular
automaton.

Dr. Wolfram illustrates his thesis with the simplest possible cellular
automata, a group of cells in a one-dimensional line. At each point in time, each

cell can have one of two values: black or white. The value of each cell is
recomputed for each cycle. The value of a cell for the next cycle is a function of
its current value as well as the value of its two adjacent neighbors. Each cellular
automaton is characterized by a rule that determines how we compute whether a
cell is black or white in the next cycle.

Consider the example of what Dr. Wolfram calls rule 222.

rule 222

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The eight possible combinations of value for the cell being recomputed and
its left and right neighbors are shown in the top row. Its new value is shown in
the bottom row. So, for example, if the cell is black and its two neighbors are
also black, then the cell will remain black in the next generation (see the leftmost
subrule of rule 222). If the cell is white, its left neighbor is white, and its right
neighbor is black, then it will be changed to black in the next generation (see the
subrule of rule 222 that is second from the right).

The universe for this simple cellular automaton is just one row of cells. If
we start with just one black cell in the middle and show the evolution of the cells
over multiple generations (where each row as we move down represents a new
generation of values), the results of rule 222 look like this:

rule 222

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An automaton is based on a rule, and a rule defines whether the cell will be
black or white based on which of the eight possible patterns exist in the current
generation. Thus there are 2 8 = 256 possible rules. Dr. Wolfram listed all 256
possible such automata and assigned each a Wolfram code from 0 to 255.
Interestingly, these 256 theoretical machines have very different properties. The
automata in what Dr. Wolfram calls class I, such as rule 222, create very
predictable patterns. If I were to ask what the value of the middle cell was after a
trillion trillion iterations of rule 222, you could answer easily: black.

Much more interesting, however, are the class IV automata, illustrated by
rule 110.

rule 110

□□□

□ 1 ■

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0 1

1

0

i i

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Multiple generations of this automaton look like this:

The interesting thing about the rule 110 automaton and class IV automata in
general is that the results are completely unpredictable. The results pass the
strictest mathematical tests for randomness, yet they do not simply generate
noise: There are repeating patterns, but they repeat in odd and unpredictable
ways. If I were to ask you what the value of a particular cell was after a trillion
trillion iterations, there would be no way to answer that question without
actually running this machine through that many generations. The solution is
clearly determined, because this is a very simple deterministic machine, but it is
completely unpredictable without actually running the machine.

Dr. Wolfram’s primary thesis is that the world is one big class IV cellular
automaton. The reason that his book is titled A New Kind of Science is because
this theory contrasts with most other scientific laws. If there is a satellite orbiting
Earth, we can predict where it will be five years from now without having to run
through each moment of a simulated process by using the relevant laws of
gravity and solve where it will be at points in time far in the future. But the
future state of class IV cellular automata cannot be predicted without simulating
every step along the way. If the universe is a giant cellular automaton, as Dr.
Wolfram postulates, there would be no computer big enough—since every
computer would be a subset of the universe—that could run such a simulation.
Therefore the future state of the universe is completely unknowable even though
it is deterministic.

Thus even though our decisions are determined (because our bodies and
brains are part of a deterministic universe), they are nonetheless inherently
unpredictable because we live in (and are part of) a class IV automaton. We
cannot predict the future of a class IV automaton except to let the future unfold.
For Dr. Wolfram, this is sufficient to allow for free will.

We don’t have to look to the universe to see future events that are
determined yet unpredictable. None of the scientists who have worked on

Watson can predict what it will do, because the program is just too complex and
varied, and its performance is based on knowledge that is far too extensive for
any human to master. If we believe that humans exhibit free will, then it follows
that we have to allow that future versions of Watson or Watson-like machines
can exhibit it also.

My own leap of faith is that I believe that humans have free will, and while
I act as if that is the case, I am hard pressed to find examples among my own
decisions that illustrate that. Consider the decision to write this book—I never
made that decision. Rather, the idea of the book decided that for me. In general, I
find myself captive to ideas that seem to implant themselves in my neocortex
and take over. How about the decision to get married, which I made (in
collaboration with one other person) thirty-six years ago? At the time, I had been
following the usual program of being attracted to—and pursuing—a pretty girl. I
then fell in love. Where is the free will in that?

But what about the little decisions I make every day—for example, the
specific words I choose to write in my book? I start with a blank virtual sheet of
paper. No one is telling me what to do. There is no editor looking over my
shoulder. My choices are entirely up to me. I am free —totally free —to write
whatever I...

Uh, grok ...

Grok ? Okay, I did it—I finally applied my free will. I was going to write the
word “want,” but I made a free decision to write something totally unexpected
instead. This is perhaps the first time I’ve succeeded in exercising pure free will.

Or not.

It should be apparent that that was a display not of will, but rather of trying
to illustrate a point (and perhaps a weak sense of humor).

Although I share Descartes’ confidence that I am conscious, I’m not so sure
about free will. It is difficult to escape Schopenhauer’s conclusion that “you can
do what you will, but in any given moment of your life you can will only one
definite thing and absolutely nothing other than that one thing.”— Nonetheless I
will continue to act as if I have free will and to believe in it, so long as I don’t
have to explain why.

Identity

A philosopher once had the following dream.

First Aristotle appeared, and the philosopher said to him, “Could you
give me a fifteen-minute capsule sketch of your entire philosophy?” To the
philosopher’s surprise, Aristotle gave him an excellent exposition in which
he compressed an enormous amount of material into a mere fifteen minutes.
But then the philosopher raised a certain objection which Aristotle couldn’t
answer. Confounded, Aristotle disappeared.

Then Plato appeared. The same thing happened again, and the
philosopher’s objection to Plato was the same as his objection to Aristotle.
Plato also couldn’t answer it and disappeared.

Then all the famous philosophers of history appeared one by one and
our philosopher refuted every one with the same objection.

After the last philosopher vanished, our philosopher said to himself, “I
know I’m asleep and dreaming all this. Yet I’ve found a universal refutation
for all philosophical systems! Tomorrow when I wake up, I will probably
have forgotten it, and the world will really miss something!” With an iron
effort, the philosopher forced himself to wake up, rush over to his desk, and
write down his universal refutation. Then he jumped back into bed with a
sigh of relief.

The next morning when he awoke, he went over to the desk to see
what he had written. It was, “That’s what you say.”

—Raymond Smullyan, as quoted by David Chalmers—

What I wonder about ever more than whether or not I am conscious or exercise
free will is why I happen to be conscious of the experiences and decisions of this
one particular person who writes books, enjoys hiking and biking, takes
nutritional supplements, and so on. An obvious answer would be, “Because
that’s who you are.”

That exchange is probably no more tautological than my answers above to
questions about consciousness and free will. But actually I do have a better
answer for why my consciousness is associated with this particular person: It is
because that is who I created myself to be.

A common aphorism is, “You are what you eat.” It is even more true to say,
“You are what you think.” As we have discussed, all of the hierarchical
structures in my neocortex that define my personality, skills, and knowledge are
the result of my own thoughts and experiences. The people I choose to interact
with and the ideas and projects I choose to engage in are all primary
determinants of who I become. For that matter, what I eat also reflects the
decisions made by my neocortex. Accepting the positive side of the free will
duality for the moment, it is my own decisions that result in who I am.

Regardless of how we came to be who we are, each of us has the desire for
our identity to persist. If you didn’t have the will to survive, you wouldn’t be
here reading this book. Every creature has that goal—it is the principal
determinant of evolution. The issue of identity is perhaps even harder to define
than consciousness or free will, but is arguably more important. After all, we
need to know what we are if we seek to preserve our existence.

Consider this thought experiment: You are in the future with technologies
more advanced than today’s. While you are sleeping, some group scans your
brain and picks up every salient detail. Perhaps they do this with blood cell¬
sized scanning machines traveling in the capillaries of your brain or with some
other suitable noninvasive technology, but they have all of the information about
your brain at a particular point in time. They also pick up and record any bodily
details that might reflect on your state of mind, such as the endocrine system.
They instantiate this “mind file” in a nonbiological body that looks and moves
like you and has the requisite subtlety and suppleness to pass for you. In the
morning you are informed about this transfer and you watch (perhaps without
being noticed) your mind clone, whom we’ll call You 2. You 2 is talking about
his or her life as if s/he were you, and relating how s/he discovered that very
morning that s/he had been given a much more durable new version 2.0 body.
“Hey, I kind of like this new body!” s/he exclaims.

The first question to consider is: Is You 2 conscious? Well, s/he certainly
seems to be. S/he passes the test I articulated earlier, in that s/he has the subtle
cues of being a feeling, conscious person. If you are conscious, then so too is
You 2.

So if you were to, uh, disappear, no one would notice. You 2 would go
around claiming to be you. All of your friends and loved ones would be content
with the situation and perhaps pleased that you now have a more durable body
and mental substrate than you used to have. Perhaps your more philosophically
minded friends would express concerns, but for the most part, everybody would
be happy, including you, or at least the person who is convincingly claiming to
be you.

So we don’t need your old body and brain anymore, right? Okay if we
dispose of it?

You’re probably not going to go along with this. I indicated that the scan
was noninvasive, so you are still around and still conscious. Moreover your
sense of identity is still with you, not with You 2, even though You 2 thinks s/he
is a continuation of you. You 2 might not even be aware that you exist or ever
existed. In fact you would not be aware of the existence of You 2 either, if we
hadn’t told you about it.

Our conclusion? You 2 is conscious but is a different person than you—You
2 has a different identity. S/he is extremely similar, much more so than a mere
genetic clone, because s/he also shares all of your neocortical patterns and
connections. Or I should say s/he shared those patterns at the moment s/he was
created. At that point, the two of you started to go your own ways, neocortically
speaking. You are still around. You are not having the same experiences as You
2. Bottom line: You 2 is not you.

Okay, so far so good. Now consider another thought experiment—one that
is, I believe, more realistic in terms of what the future will bring. You undergo a
procedure to replace a very small part of your brain with a nonbiological unit.
You’re convinced that it’s safe, and there are reports of various benefits.

This is not so far-fetched, as it is done routinely for people with
neurological and sensory impairments, such as the neural implant for
Parkinson’s disease and cochlear implants for the deaf. In these cases the
computerized device is placed inside the body but outside the brain yet
connected into the brain (or in the case of the cochlear implants, to the auditory
nerve). In my view the fact that the actual computer is physically placed outside
the actual brain is not philosophically significant: We are effectively augmenting
the brain and replacing with a computerized device those of its functions that no
longer work properly. In the 2030s, when intelligent computerized devices will
be the size of blood cells (and keep in mind that white blood cells are sufficiently
intelligent to recognize and combat pathogens), we will introduce them
noninvasively, no surgery required.

Returning to our future scenario, you have the procedure, and as promised,
it works just fine—certain of your capabilities have improved. (You have better
memory, perhaps.) So are you still you? Your friends certainly think so. You
think so. There is no good argument that you’re suddenly a different person.
Obviously, you underwent the procedure in order to effect a change in
something, but you are still the same you. Your identity hasn’t changed.
Someone else’s consciousness didn’t suddenly take over your body.

Okay, so, encouraged by these results, you now decide to have another

procedure, this time involving a different region of the brain. The result is the
same: You experience some improvement in capability, but you’re still you.

It should be apparent where I am going with this. You keep opting for
additional procedures, your confidence in the process only increasing, until
eventually you’ve changed every part of your brain. Each time the procedure
was carefully done to preserve all of your neocortical patterns and connections
so that you have not lost any of your personality, skills, or memories. There was
never a you and a You 2; there was only you. No one, including you, ever
notices you ceasing to exist. Indeed—there you are.

Our conclusion: You still exist. There’s no dilemma here. Everything is

fine.

Except for this: You, after the gradual replacement process, are entirely
equivalent to You 2 in the prior thought experiment (which I will call the scan-
and-instantiate scenario). You, after the gradual replacement scenario, have all of
the neocortical patterns and connections that you had originally, only in a
nonbiological substrate, which is also true of You 2 in the scan-and-instantiate
scenario. You, after the gradual replacement scenario, have some additional
capabilities and greater durability than you did before the process, but this is
likewise true of You 2 in the scan-and-instantiate process.

But we concluded that You 2 is not you. And if you, after the gradual
replacement process, are entirely equivalent to You 2 after the scan-and-
instantiate process, then you after the gradual replacement process must also not
be you.

That, however, contradicts our earlier conclusion. The gradual replacement
process consists of multiple steps. Each of those steps appeared to preserve
identity, just as we conclude today that a Parkinson’s patient has the same
identity after having had a neural implant installed.—

It is just this sort of philosophical dilemma that leads some people to
conclude that these replacement scenarios will never happen (even though they
are already taking place). But consider this: We naturally undergo a gradual
replacement process throughout our lives. Most of our cells in our body are
continuously being replaced. (You just replaced 100 million of them in the
course of reading the last sentence.) Cells in the inner lining of the small
intestine turn over in about a week, as does the stomach’s protective lining. The
life span of white blood cells ranges from a few days to a few months, depending
on the type. Platelets last about nine days.

Neurons persist, but their organelles and their constituent molecules turn
over within a month.— The half-life of a neuron microtubule is about ten

minutes; the actin filaments in the dendrites last about forty seconds; the proteins
that provide energy to the synapses are replaced every hour; the NMDA
receptors in synapses are relatively long-lived at five days.

So you are completely replaced in a matter of months, which is comparable
to the gradual replacement scenario I describe above. Are you the same person
you were a few months ago? Certainly there are some differences. Perhaps you
learned a few things. But you assume that your identity persists, that you are not
continually destroyed and re-created.

Consider a river, like the one that flows past my office. As I look out now at
what people call the Charles River, is it the same river that I saw yesterday?
Let’s first reflect on what a river is. The dictionary defines it is “a large natural
stream of flowing water.” By that definition, the river I’m looking at is a
completely different one than it was yesterday. Every one of its water molecules
has changed, a process that happens very quickly. Greek philosopher Diogenes
Laertius wrote in the third century AD that “you cannot step into the same river
twice.”

But that is not how we generally regard rivers. People like to look at them
because they are symbols of continuity and stability. By the common view, the
Charles River that I looked at yesterday is the same river I see today. Our lives
are much the same. Fundamentally we are not the stuff that makes up our bodies
and brains. These particles essentially flow through us in the same way that
water molecules flow through a river. We are a pattern that changes slowly but
has stability and continuity, even though the stuff constituting the pattern
changes quickly.

The gradual introduction of nonbiological systems into our bodies and
brains will be just another example of the continual turnover of parts that
compose us. It will not alter the continuity of our identity any more than the
natural replacement of our biological cells does. We have already largely
outsourced our historical, intellectual, social, and personal memories to our
devices and the cloud. The devices we interact with to access these memories
may not yet be inside our bodies and brains, but as they become smaller and
smaller (and we are shrinking technology at a rate of about a hundred in 3-D
volume per decade), they will make their way there. In any event, it will be a
useful place to put them—we won’t lose them that way. If people do opt out of
placing microscopic devices inside their bodies, that will be fine, as there will be
other ways to access the pervasive cloud intelligence.

But we come back to the dilemma I introduced earlier. You, after a period
of gradual replacement, are equivalent to You 2 in the scan-and-instantiate
scenario, but we decided that You 2 in that scenario does not have the same

identity as you. So where does that leave us?

It leaves us with an appreciation of a capability that nonbiological systems
have that biological systems do not: the ability to be copied, backed up, and re¬
created. We do that routinely with our devices. When we get a new smartphone,
we copy over all of our files, so it has much the same personality, skills, and
memories that the old smartphone did. Perhaps it also has some new capabilities,
but the contents of the old phone are still with us. Similarly, a program such as
Watson is certainly backed up. If the Watson hardware were destroyed
tomorrow, Watson would easily be re-created from its backup files stored in the
cloud.

This represents a capability in the nonbiological world that does not exist in
the biological world. It is an advantage, not a limitation, which is one reason
why we are so eager today to continue uploading our memories to the cloud. We
will certainly continue in this direction, as nonbiological systems attain more and
more of the capabilities of our biological brains.

My resolution of the dilemma is this: It is not true that You 2 is not you—it
is you. It is just that there are now two of you. That’s not so bad—if you think
you are a good thing, then two of you is even better.

What I believe will actually happen is that we will continue on the path of
the gradual replacement and augmentation scenario until ultimately most of our
thinking will be in the cloud. My leap of faith on identity is that identity is
preserved through continuity of the pattern of information that makes us us.
Continuity does allow for continual change, so whereas I am somewhat different
than I was yesterday, I nonetheless have the same identity. However, the
continuity of the pattern that constitutes my identity is not substrate-dependent.
Biological substrates are wonderful—they have gotten us very far—but we are
creating a more capable and durable substrate for very good reasons.

CHAPTER 10

THE LAW OF

ACCELERATING RETURNS
APPLIED TO THE BRAIN

And though man should remain, in some respects, the higher creature, is not
this in accordance with the practice of nature, which allows superiority in
some things to animals which have, on the whole, been long surpassed? Has
she not allowed the ant and the bee to retain superiority over man in the
organization of their communities and social arrangements, the bird in
traversing the air, the fish in swimming, the horse in strength and fleetness,
and the dog in self-sacrifice?

—Samuel Butler, 1871

There was a time, when the earth was to all appearance utterly destitute
both of animal and vegetable life, and when according to the opinion of our
best philosophers it was simply a hot round ball with a crust gradually
cooling. Now if a human being had existed while the earth was in this state
and had been allowed to see it as though it were some other world with
which he had no concern, and if at the same time he were entirely ignorant
of all physical science, would he not have pronounced it impossible that
creatures possessed of anything like consciousness should be evolved from
the seeming cinder which he was beholding? Would he not have denied that
it contained any potentiality of consciousness? Yet in the course of time
consciousness came. Is it not possible then that there may be even yet new
channels dug out for consciousness, though we can detect no signs of them
at present?

—Samuel Butler, 1871

When we reflect upon the manifold phases of life and consciousness which

have been evolved already, it would be rash to say that no others can be
developed, and that animal life is the end of all things. There was a time
when fire was the end of all things: another when rocks and water were so.

—Samuel Butler, 1871

There is no security against the ultimate development of mechanical
consciousness, in the fact of machines possessing little consciousness now.
A mollusk has not much consciousness. Reflect upon the extraordinary
advance which machines have made during the last few hundred years, and
note how slowly the animal and vegetable kingdoms are advancing. The
more highly organized machines are creatures not so much of yesterday, as
of the last five minutes, so to speak, in comparison with past time. Assume
for the sake of argument that conscious beings have existed for some
twenty million years: see what strides machines have made in the last
thousand! May not the world last twenty million years longer? If so, what
will they not in the end become?

—Samuel Butler, 1871

My core thesis, which I call the law of accelerating returns (LOAR), is that
fundamental measures of information technology follow predictable and
exponential trajectories, belying the conventional wisdom that “you can’t predict
the future.” There are still many things—which project, company, or technical
standard will prevail in the marketplace, when peace will come to the Middle
East—that remain unknowable, but the underlying price/performance and
capacity of information has nonetheless proven to be remarkably predictable.
Surprisingly, these trends are unperturbed by conditions such as war or peace
and prosperity or recession.

A primary reason that evolution created brains was to predict the future. As
one of our ancestors walked through the savannas thousands of years ago, she
might have noticed that an animal was progressing toward a route that she was
taking. She would predict that if she stayed on course, their paths would
intersect. Based on this, she decided to head in another direction, and her
foresight proved valuable to survival.

But such built-in predictors of the future are linear, not exponential, a
quality that stems from the linear organization of the neocortex. Recall that the
neocortex is constantly making predictions—what letter and word we will see

next, whom we expect to see as we round the corner, and so on. The neocortex is
organized with linear sequences of steps in each pattern, which means that
exponential thinking does not come naturally to us. The cerebellum also uses
linear predictions. When it helps us to catch a fly ball it is making a linear
prediction about where the ball will be in our visual field of view and where our
gloved hand should be in our visual field of view to catch it.

As I have pointed out, there is a dramatic difference between linear and
exponential progressions (forty steps linearly is forty, but exponentially is a
trillion), which accounts for why my predictions stemming from the law of
accelerating returns seem surprising to many observers at first. We have to train
ourselves to think exponentially. When it comes to information technologies, it is
the right way to think.

The quintessential example of the law of accelerating returns is the
perfectly smooth, doubly exponential growth of the price/performance of
computation, which has held steady for 110 years through two world wars, the
Great Depression, the Cold War, the collapse of the Soviet Union, the
reemergence of China, the recent financial crisis, and all of the other notable
events of the late nineteenth, twentieth, and early twenty-first centuries. Some
people refer to this phenomenon as “Moore’s law,” but that is a misconception.
Moore’s law—which states that you can place twice as many components on an
integrated circuit every two years, and they run faster because they are smaller—
is just one paradigm among many. It was in fact the fifth, not the first, paradigm
to bring exponential growth to the price/performance of computing.

The exponential rise of computation started with the 1890 U.S. census (the
first to be automated) using the first paradigm of electromechanical calculation,
decades before Gordon Moore was even born. In The Singularity Is Near I
provide this graph through 2002, and here I update it through 2009 (see the
graph on page 257 titled “Exponential Growth of Computing for 110 Years”).
The smoothly predictable trajectory has continued, even through the recent
economic downturn.

Computation is the most important example of the law of accelerating
returns, because of the amount of data we have for it, the ubiquity of
computation, and its key role in ultimately revolutionizing everything we care
about. But it is far from the only example. Once a technology becomes an
information technology, it becomes subject to the LOAR.

Biomedicine is becoming the most significant recent area of technology and
industry to be transformed in this way. Progress in medicine has historically been
based on accidental discoveries, so progress during the earlier era was linear, not
exponential. This has nevertheless been beneficial: Life expectancy has grown

from twenty-three years as of a thousand years ago, to thirty-seven years as of
two hundred years ago, to close to eighty years today. With the gathering of the
software of life—the genome—medicine and human biology have become an
information technology. The human genome project itself was perfectly
exponential, with the amount of genetic data doubling and the cost per base pair
coming down by half each year since the project was initiated in 1990.^ (All the
graphs in this chapter have been updated since The Singularity Is Near was
published.)

Cost per Human Genome Logarithmic Plot

Year

The cost of sequencing a human-sized genome. 1

Growth in Genbank

DNA Sequence Data Logarithmic Plot

We now have the ability to design biomedical interventions on computers
and to test them on biological simulators, the scale and precision of which are
also doubling every year. We can also update our own obsolete software: RNA
interference can turn genes off, and new forms of gene therapy can add new
genes, not just to a newborn but to a mature individual. The advance of genetic
technologies also affects the brain reverse-engineering project, in that one
important aspect of it is understanding how genes control brain functions such as
creating new connections to reflect recently added cortical knowledge. There are
many other manifestations of this integration of biology and information
technology, as we move beyond genome sequencing to genome synthesizing.

Another information technology that has seen smooth exponential growth is
our ability to communicate with one another and transmit vast repositories of
human knowledge. There are many ways to measure this phenomenon. Cooper’s
law, which states that the total bit capacity of wireless communications in a
given amount of radio spectrum doubles every thirty months, has held true from
the time Guglielmo Marconi used the wireless telegraph for Morse code
transmissions in 1897 to today’s 4G communications technologies.- According
to Cooper’s law, the amount of information that can be transmitted over a given
amount of radio spectrum has been doubling every two and a half years for more
than a century. Another example is the number of bits per second transmitted on
the Internet, which is doubling every one and a quarter years. -

The reason I became interested in trying to predict certain aspects of
technology is that I realized about thirty years ago that the key to becoming

successful as an inventor (a profession I adopted when I was five years old) was
timing. Most inventions and inventors fail not because the gadgets themselves
don’t work, but because their timing is wrong, appearing either before all of the
enabling factors are in place or too late, having missed the window of
opportunity.

Internet Data Traffic (Global) Logarithmic Plot

Year

The international (country-to-country) bandwidth dedicated to the
Internet for the world.-

Highest Internet
Backbone Bandwidth

Logarithmic Plot

10 12
10 11
J 10 10

8 io 9

o>

*2 10 s

a

Q.

a 10 7

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10 s
10 *

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Year

The highest bandwidth (speed) of the Internet backbone. 2

Being an engineer, about three decades ago I started to gather data on
measures of technology in different areas. When I began this effort, I did not
expect that it would present a clear picture, but I did hope that it would provide
some guidance and enable me to make educated guesses. My goal was—and still
is—to time my own technology efforts so that they will be appropriate for the
world that exists when I complete a project—which I realized would be very
different from the world that existed when I started.

Consider how much and how quickly the world has changed only recently.
Just a few years ago, people did not use social networks (Facebook, for example,
was founded in 2004 and had 901 million monthly active users at the end of
March 2012),- wikis, blogs, or tweets. In the 1990s most people did not use
search engines or cell phones. Imagine the world without them. That seems like
ancient history but was not so long ago. The world will change even more
dramatically in the near future.

In the course of my investigation, I made a startling discovery: If a
technology is an information technology, the basic measures of
price/performance and capacity (per unit of time or cost, or other resource)
follow amazingly precise exponential trajectories.

These trajectories outrun the specific paradigms they are based on (such as
Moore’s law). But when one paradigm runs out of steam (for example, when
engineers were no longer able to reduce the size and cost of vacuum tubes in the
1950s), it creates research pressure to create the next paradigm, and so another
S-curve of progress begins.

The exponential portion of that next S-curve for the new paradigm then
continues the ongoing exponential of the information technology measure. Thus
vacuum tube-based computing in the 1950s gave way to transistors in the 1960s,
and then to integrated circuits and Moore’s law in the late 1960s, and beyond.
Moore’s law, in turn, will give way to three-dimensional computing, the early
examples of which are already in place. The reason why information
technologies are able to consistently transcend the limitations of any particular
paradigm is that the resources required to compute or remember or transmit a bit
of information are vanishingly small.

We might wonder, are there fundamental limits to our ability to compute
and transmit information, regardless of paradigm? The answer is yes, based on
our current understanding of the physics of computation. Those limits, however,
are not very limiting. Ultimately we can expand our intelligence trillions-fold
based on molecular computing. By my calculations, we will reach these limits
late in this century.

It is important to point out that not every exponential phenomenon is an
example of the law of accelerating returns. Some observers misconstrue the
LOAR by citing exponential trends that are not information-based: For example,
they point out, men’s shavers have gone from one blade to two to four, and then
ask, where are the eight-blade shavers? Shavers are not (yet) an information
technology.

In The Singularity Is Near, I provide a theoretical examination, including
(in the appendix to that book) a mathematical treatment of why the LOAR is so
remarkably predictable. Essentially, we always use the latest technology to
create the next. Technologies build on themselves in an exponential manner, and
this phenomenon is readily measurable if it involves an information technology.
In 1990 we used the computers and other tools of that era to create the
computers of 1991; in 2012 we are using current information tools to create the
machines of 2013 and 2014. More broadly speaking, this acceleration and
exponential growth applies to any process in which patterns of information
evolve. So we see acceleration in the pace of biological evolution, and similar
(but much faster) acceleration in technological evolution, which is itself an
outgrowth of biological evolution.

I now have a public track record of more than a quarter of a century of
predictions based on the law of accelerating returns, starting with those
presented in The Age of Intelligent Machines, which I wrote in the mid-1980s.
Examples of accurate predictions from that book include: the emergence in the
mid- to late 1990s of a vast worldwide web of communications tying together
people around the world to one another and to all human knowledge; a great

wave of democratization emerging from this decentralized communication
network, sweeping away the Soviet Union; the defeat of the world chess
champion by 1998; and many others.

I described the law of accelerating returns, as it is applied to computation,
extensively in The Age of Spiritual Machines, where I provided a century of data
showing the doubly exponential progression of the price/performance of
computation through 1998. It is updated through 2009 below.

I recently wrote a 146-page review of the predictions I made in The Age of
Intelligent Machines, The Age of Spiritual Machines, and The Singularity Is
Near. (You can read the essay here by going to the link in this endnote. )-The Age
of Spiritual Machines included hundreds of predictions for specific decades
(2009, 2019, 2029, and 2099). For example, I made 147 predictions for 2009 in
The Age of Spiritual Machines, which I wrote in the 1990s. Of these, 115 (78
percent) are entirely correct as of the end of 2009; the predictions that were
concerned with basic measurements of the capacity and price/performance of
information technologies were particularly accurate. Another 12 (8 percent) are
“essentially correct.” A total of 127 predictions (86 percent) are correct or
essentially correct. (Since the predictions were made specific to a given decade,
a prediction for 2009 was considered “essentially correct” if it came true in 2010
or 2011.) Another 17 (12 percent) are partially correct, and 3 (2 percent) are
wrong.

Exponential Growth
of Computing for 110 Years

Moore's law was the fifth, not the first, paradigm to

bring exponential growth in ccmpuling Logarithmic Plot

10 15 -g

1900 '10 '20 '30 '40 '50 '60 70 '80 '90 2000 '10

Year

Calculations per second per (constant) thousand dollars of different
computing devices.—

Growth in Supercomputer Power Logarithmic Plot

Year

Floating-point operations per second of different supercomputers.—

Bits per Dollar

Logarithmic Plot

10 '°

10 9
10 8
10 7
I 10 s
I 10 5
10 *

10 3
10 2
10 1

1970 75 1980 ’85 1990 ’95 2000 ’05 2010

Year

Transistors per chip for different Intel processors.—

Transistors per Chip

Dynamic RAM Memory

Logarithmic Plot

Bits per dollar for dynamic random access memory chips

13

Logaritfvnic Plot

Random Access Memory

Year

Bits per dollar for random access memory chips.—

Average Transistor Price Logarithmic Plot

Logarithmic Plot

Magnetic Data Storage Logarithmic Plot

1950 1960 1970 1980 1990 2000 2010

Year

Bits per dollar (in constant 2000 dollars) for magnetic data storage.—
Even the predictions that were “wrong” were not all wrong. For example, I
judged my prediction that we would have self-driving cars to be wrong, even
though Google has demonstrated self-driving cars, and even though in October
2010 four driverless electric vans successfully concluded a 13,000-kilometer test
drive from Italy to China.— Experts in the field currently predict that these
technologies will be routinely available to consumers by the end of this decade.
Exponentially expanding computational and communication technologies

all contribute to the project to understand and re-create the methods of the
human brain. This effort is not a single organized project but rather the result of
a great many diverse projects, including detailed modeling of constituents of the
brain ranging from individual neurons to the entire neocortex, the mapping of
the “connectome” (the neural connections in the brain), simulations of brain
regions, and many others. All of these have been scaling up exponentially. Much
of the evidence presented in this book has only become available recently—for
example, the 2012 Wedeen study discussed in chapter 4 that showed the very
orderly and “simple” (to quote the researchers) gridlike pattern of the
connections in the neocortex. The researchers in that study acknowledge that
their insight (and images) only became feasible as the result of new high-
resolution imaging technology.

Brain scanning technologies are improving in resolution, spatial and
temporal, at an exponential rate. Different types of brain scanning methods being
pursued range from completely noninvasive methods that can be used with
humans to more invasive or destructive methods on animals.

MRI (magnetic resonance imaging), a noninvasive imaging technique with
relatively high temporal resolution, has steadily improved at an exponential
pace, to the point that spatial resolutions are now close to 100 microns
(millionths of a meter).

4

Noninvasive

In Vivo

Invasive

gm

Destructive

In Vitro

r

A Venn diagram of brain imaging methods.—

Spatial Resolution

10cm-,

Time Resolution or Duration
Tools for imaging the brain.—

MR I Spatial Resolution Logarithmic Plot

Year

MRI spatial resolution in microns.—

Spatial Resolution of Destructive

Brain Imaging Techniques Logarithmic Plot

1,000

10,000

1983 1987 1991 1995 1999 2003 2007

Year

Spatial resolution of destructive imaging techniques.—

2011

Nondestructive Brain Imaging
Resolution in Animals Logarithmic Plot

Year

Spatial resolution of nondestructive imaging techniques in animals.—
Destructive imaging, which is performed to collect the connectome (map of
all interneuronal connections) in animal brains, has also improved at an
exponential pace. Current maximum resolution is around four nanometers,
which is sufficient to see individual connections.

Artificial intelligence technologies such as natural-language-understanding

systems are not necessarily designed to emulate theorized principles of brain
function, but rather for maximum effectiveness. Given this, it is notable that the
techniques that have won out are consistent with the principles I have outlined in
this book: self-organizing, hierarchical recognizers of invariant self-associative
patterns with redundancy and up-and-down predictions. These systems are also
scaling up exponentially, as Watson has demonstrated.

A primary purpose of understanding the brain is to expand our toolkit of
techniques to create intelligent systems. Although many AI researchers may not
fully appreciate this, they have already been deeply influenced by our knowledge
of the principles of the operation of the brain. Understanding the brain also helps
us to reverse brain dysfunctions of various kinds. There is, of course, another
key goal of the project to reverse-engineer the brain: understanding who we are.

CHAPTER 11

OBJECTIONS

If a machine can prove indistinguishable from a human, we should award it
the respect we would to a human—we should accept that it has a mind.

—Stevan Harnad

T he most significant source of objection to my thesis on the law of accelerating
returns and its application to the amplification of human intelligence stems from
the linear nature of human intuition. As I described earlier, each of the several
hundred million pattern recognizers in the neocortex processes information
sequentially. One of the implications of this organization is that we have linear
expectations about the future, so critics apply their linear intuition to information
phenomena that are fundamentally exponential.

I call objections along these lines “criticism from incredulity,” in that
exponential projections seem incredible given our linear predilection, and they
take a variety of forms. Microsoft cofounder Paul Allen (born in 1953) and his
colleague Mark Greaves recently articulated several of them in an essay titled
“The Singularity Isn’t Near” published in Technology Review magazine.- While
my response here is to Allen’s particular critiques, they represent a typical range
of objections to the arguments I’ve made, especially with regard to the brain.
Although Allen references The Singularity Is Near in the title of his essay, his
only citation in the piece is to an essay I wrote in 2001 (“The Law of
Accelerating Returns”). Moreover, his article does not acknowledge or respond
to arguments I actually make in the book. Unfortunately, I find this often to be
the case with critics of my work.

When The Age of Spiritual Machines was published in 1999, augmented
later by the 2001 essay, it generated several lines of criticism, such as: Moore’s
law will come to an end; hardware capability may be expanding exponentially
but software is stuck in the mud; the brain is too complicated; there are
capabilities in the brain that inherently cannot be replicated in software; and

several others. One of the reasons I wrote The Singularity Is Near was to
respond to those critiques.

I cannot say that Allen and similar critics would necessarily have been
convinced by the arguments I made in that book, but at least he and others could
have responded to what I actually wrote. Allen argues that “the Law of
Accelerating Returns (LOAR)...is not a physical law.” I would point out that
most scientific laws are not physical laws, but result from the emergent
properties of a large number of events at a lower level. A classic example is the
laws of thermodynamics (LOT). If you look at the mathematics underlying the
LOT, it models each particle as following a random walk, so by definition we
cannot predict where any particular particle will be at any future time. Yet the
overall properties of the gas are quite predictable to a high degree of precision,
according to the laws of thermodynamics. So it is with the law of accelerating
returns: Each technology project and contributor is unpredictable, yet the overall
trajectory, as quantified by basic measures of price/performance and capacity,
nonetheless follows a remarkably predictable path.

If computer technology were being pursued by only a handful of
researchers, it would indeed be unpredictable. But it’s the product of a
sufficiently dynamic system of competitive projects that a basic measure of its
price/performance, such as calculations per second per constant dollar, follows a
very smooth exponential path, dating back to the 1890 American census as I
noted in the previous chapter . While the theoretical basis for the LOAR is
presented extensively in The Singularity Is Near, the strongest case for it is made
by the extensive empirical evidence that I and others present.

Allen writes that “these ‘laws’ work until they don’t.” Here he is confusing
paradigms with the ongoing trajectory of a basic area of information technology.
If we were examining, for example, the trend of creating ever smaller vacuum
tubes—the paradigm for improving computation in the 1950s—it’s true that it
continued until it didn’t. But as the end of this particular paradigm became clear,
research pressure grew for the next paradigm. The technology of transistors kept
the underlying trend of the exponential growth of price/performance of
computation going, and that led to the fifth paradigm (Moore’s law) and the
continual compression of features on integrated circuits. There have been regular
predictions that Moore’s law will come to an end. The semiconductor industry’s
“International Technology Roadmap for Semiconductors” projects seven-
nanometer features by the early 2020s. 2 At that point key features will be the
width of thirty-five carbon atoms, and it will be difficult to continue shrinking
them any farther. However, Intel and other chip makers are already taking the
first steps toward the sixth paradigm, computing in three dimensions, to continue

exponential improvement in price/performance. Intel projects that three-
dimensional chips will be mainstream by the teen years; three-dimensional
transistors and 3-D memory chips have already been introduced. This sixth
paradigm will keep the LOAR going with regard to computer price/performance
to a time later in this century when a thousand dollars’ worth of computation will
be trillions of times more powerful than the human brain.^ (It appears that Allen
and I are at least in agreement on what level of computation is required to
functionally simulate the human brain.)-

Allen then goes on to give the standard argument that software is not
progressing in the same exponential manner as hardware. In The Singularity Is
Near I addressed this issue at length, citing different methods of measuring
complexity and capability in software that do demonstrate a similar exponential
growth.- One recent study (“Report to the President and Congress, Designing a
Digital Future: Federally Funded Research and Development in Networking and
Information Technology,” by the President’s Council of Advisors on Science and
Technology) states the following:

Even more remarkable—and even less widely understood—is that in
many areas, performance gains due to improvements in algorithms have
vastly exceeded even the dramatic performance gains due to increased
processor speed. The algorithms that we use today for speech recognition,
for natural language translation, for chess playing, for logistics planning,
have evolved remarkably in the past decade.... Here is just one example,
provided by Professor Martin Grotschel of Konrad-Zuse-Zentrum fur
Informationstechnik Berlin. Grotschel, an expert in optimization, observes
that a benchmark production planning model solved using linear
programming would have taken 82 years to solve in 1988, using the
computers and the linear programming algorithms of the day. Fifteen years
later—in 2003—this same model could be solved in roughly 1 minute, an
improvement by a factor of roughly 43 million. Of this, a factor of roughly
1,000 was due to increased processor speed, whereas a factor of roughly
43,000 was due to improvements in algorithms! Grotschel also cites an
algorithmic improvement of roughly 30,000 for mixed integer
programming between 1991 and 2008. The design and analysis of
algorithms, and the study of the inherent computational complexity of
problems, are fundamental subfields of computer science.

Note that the linear programming that Grotschel cites above as having
benefited from an improvement in performance of 43 million to 1 is the

mathematical technique that is used to optimally assign resources in a
hierarchical memory system such as HHMM that I discussed earlier. I cite many
other similar examples like this in The Singularity Is Near.-

Regarding AI, Allen is quick to dismiss IBM’s Watson, an opinion shared
by many other critics. Many of these detractors don’t know anything about
Watson other than the fact that it is software running on a computer (albeit a
parallel one with 720 processor cores). Allen writes that systems such as Watson
“remain brittle, their performance boundaries are rigidly set by their internal
assumptions and defining algorithms, they cannot generalize, and they
frequently give nonsensical answers outside of their specific areas.”

First of all, we could make a similar observation about humans. I would
also point out that Watson’s “specific areas” include all of Wikipedia plus many
other knowledge bases, which hardly constitute a narrow focus. Watson deals
with a vast range of human knowledge and is capable of dealing with subtle
forms of language, including puns, similes, and metaphors in virtually all fields
of human endeavor. It’s not perfect, but neither are humans, and it was good
enough to be victorious on Jeopardy! over the best human players.

Allen argues that Watson was assembled by the scientists themselves,
building each link of narrow knowledge in specific areas. This is simply not true.
Although a few areas of Watson’s data were programmed directly, Watson
acquired the significant majority of its knowledge on its own by reading natural-
language documents such as Wikipedia. That represents its key strength, as does
its ability to understand the convoluted language in Jeopardy! queries (answers
in search of a question).

As I mentioned earlier, much of the criticism of Watson is that it works
through statistical probabilities rather than “true” understanding. Many readers
interpret this to mean that Watson is merely gathering statistics on word
sequences. The term “statistical information” in the case of Watson actually
refers to distributed coefficients and symbolic connections in self-organizing
methods such as hierarchical hidden Markov models. One could just as easily
dismiss the distributed neurotransmitter concentrations and redundant
connection patterns in the human cortex as “statistical information.” Indeed we
resolve ambiguities in much the same way that Watson does—by considering the
likelihood of different interpretations of a phrase.

Allen continues, “Every structure [in the brain] has been precisely shaped
by millions of years of evolution to do a particular thing, whatever it might be. It
is not like a computer, with billions of identical transistors in regular memory
arrays that are controlled by a CPU with a few different elements. In the brain
every individual structure and neural circuit has been individually refined by

evolution and environmental factors.”

This contention that every structure and neural circuit in the brain is unique
and there by design is simply impossible, for it would mean that the blueprint of
the brain would require hundreds of trillions of bytes of information. The brain’s
structural plan (like that of the rest of the body) is contained in the genome, and
the brain itself cannot contain more design information than the genome. Note
that epigenetic information (such as the peptides controlling gene expression)
does not appreciably add to the amount of information in the genome.
Experience and learning do add significantly to the amount of information
contained in the brain, but the same can be said of AI systems like Watson. I
show in The Singularity Is Near that, after lossless compression (due to massive
redundancy in the genome), the amount of design information in the genome is
about 50 million bytes, roughly half of which (that is, about 25 million bytes)
pertains to the brain.- That’s not simple, but it is a level of complexity we can
deal with and represents less complexity than many software systems in the
modern world. Moreover much of the brain’s 25 million bytes of genetic design
information pertain to the biological requirements of neurons, not to their
information-proce ssing algorithms.

How do we arrive at on the order of 100 to 1,000 trillion connections in the
brain from only tens of millions of bytes of design information? Obviously, the
answer is through massive redundancy. Dharmendra Modha, manager of
Cognitive Computing for IBM Research, writes that “neuroanatomists have not
found a hopelessly tangled, arbitrarily connected network, completely
idiosyncratic to the brain of each individual, but instead a great deal of repeating
structure within an individual brain and a great deal of homology across
species.... The astonishing natural reconfigurability gives hope that the core
algorithms of neurocomputation are independent of the specific sensory or motor
modalities and that much of the observed variation in cortical structure across
areas represents a refinement of a canonical circuit; it is indeed this canonical
circuit we wish to reverse engineer.

Allen argues in favor of an inherent “complexity brake that would
necessarily limit progress in understanding the human brain and replicating its
capabilities,” based on his notion that each of the approximately 100 to 1,000
trillion connections in the human brain is there by explicit design. His
“complexity brake” confuses the forest with the trees. If you want to understand,
model, simulate, and re-create a pancreas, you don’t need to re-create or simulate
every organelle in every pancreatic islet cell. You would want instead to
understand one islet cell, then abstract its basic functionality as it pertains to

insulin control, and then extend that to a large group of such cells. This
algorithm is well understood with regard to islet cells. There are now artificial
pancreases that utilize this functional model being tested. Although there is
certainly far more intricacy and variation in the brain than in the massively
repeated islet cells of the pancreas, there is nonetheless massive repetition of
functions, as I have described repeatedly in this book.

Critiques along the lines of Allen’s also articulate what I call the “scientist’s
pessimism.” Researchers working on the next generation of a technology or of
modeling a scientific area are invariably struggling with that immediate set of
challenges, so if someone describes what the technology will look like in ten
generations, their eyes glaze over. One of the pioneers of integrated circuits was
recalling for me recently the struggles to go from 10-micron (10,000
nanometers) feature sizes to 5-micron (5,000 nanometers) features over thirty
years ago. The scientists were cautiously confident of reaching this goal, but
when people predicted that someday we would actually have circuitry with
feature sizes under 1 micron (1,000 nanometers), most of them, focused on their
own goal, thought that was too wild to contemplate. Objections were made
regarding the fragility of circuitry at that level of precision, thermal effects, and
so on. Today Intel is starting to use chips with 22-nanometer gate lengths.

We witnessed the same sort of pessimism with respect to the Human
Genome Project. Halfway through the fifteen-year effort, only 1 percent of the
genome had been collected, and critics were proposing basic limits on how
quickly it could be sequenced without destroying the delicate genetic structures.
But thanks to the exponential growth in both capacity and price/performance, the
project was finished seven years later. The project to reverse-engineer the human
brain is making similar progress. It is only recently, for example, that we have
reached a threshold with noninvasive scanning techniques so that we can see
individual interneuronal connections forming and firing in real time. Much of the
evidence I have presented in this book was dependent on such developments and
has only recently been available.

Allen describes my proposal about reverse-engineering the human brain as
simply scanning the brain to understand its fine structure and then simulating an
entire brain “bottom up” without comprehending its information-processing
methods. This is not my proposition. We do need to understand in detail how
individual types of neurons work, and then gather information about how
functional modules are connected. The functional methods that are derived from
this type of analysis can then guide the development of intelligent systems.
Basically, we are looking for biologically inspired methods that can accelerate
work in AI, much of which has progressed without significant insight as to how

the brain performs similar functions. From my own work in speech recognition, I
know that our work was greatly accelerated when we gained insights as to how
the brain prepares and transforms auditory information.

The way that the massively redundant structures in the brain differentiate is
through learning and experience. The current state of the art in AI does in fact
enable systems to also learn from their own experience. The Google self-driving
cars learn from their own driving experience as well as from data from Google
cars driven by human drivers; Watson learned most of its knowledge by reading
on its own. It is interesting to note that the methods deployed today in AI have
evolved to be mathematically very similar to the mechanisms in the neocortex.

Another objection to the feasibility of “strong AI” (artificial intelligence at
human levels and beyond) that is often raised is that the human brain makes
extensive use of analog computing, whereas digital methods inherently cannot
replicate the gradations of value that analog representations can embody. It is
true that one bit is either on or off, but multiple-bit words easily represent
multiple gradations and can do so to any desired degree of accuracy. This is, of
course, done all the time in digital computers. As it is, the accuracy of analog
information in the brain (synaptic strength, for example) is only about one level
within 256 levels that can be represented by eight bits.

In chapter 9 I cited Roger Penrose and Stuart Hameroff’s objection, which
concerned microtubules and quantum computing. Recall that they claim that the
microtubule structures in neurons are doing quantum computing, and since it is
not possible to achieve that in computers, the human brain is fundamentally
different and presumably better. As I argued earlier, there is no evidence that
neuronal microtubules are carrying out quantum computation. Humans in fact do
a very poor job of solving the kinds of problems that a quantum computer would
excel at (such as factoring large numbers). And if any of this proved to be true,
there would be nothing barring quantum computing from also being used in our
computers.

John Searle is famous for introducing a thought experiment he calls “the
Chinese room,” an argument I discuss in detail in The Singularity Is Near.- In
short, it involves a man who takes in written questions in Chinese and then
answers them. In order to do this, he uses an elaborate rulebook. Searle claims
that the man has no true understanding of Chinese and is not “conscious” of the
language (as he does not understand the questions or the answers) despite his
apparent ability to answer questions in Chinese. Searle compares this to a
computer and concludes that a computer that could answer questions in Chinese
(essentially passing a Chinese Turing test) would, like the man in the Chinese
room, have no real understanding of the language and no consciousness of what

it was doing.

There are a few philosophical sleights of hand in Searle’s argument. For one
thing, the man in this thought experiment is comparable only to the central
processing unit (CPU) of a computer. One could say that a CPU has no true
understanding of what it is doing, but the CPU is only part of the structure. In
Searle’s Chinese room, it is the man with his rulebook that constitutes the whole
system. That system does have an understanding of Chinese; otherwise it would
not be capable of convincingly answering questions in Chinese, which would
violate Searle’s assumption for this thought experiment.

The attractiveness of Searle’s argument stems from the fact that it is
difficult today to infer true understanding and consciousness in a computer
program. The problem with his argument, however, is that you can apply his
own line of reasoning to the human brain itself. Each neocortical pattern
recognizer—indeed, each neuron and each neuronal component—is following an
algorithm. (After all, these are molecular mechanisms that follow natural law.) If
we conclude that following an algorithm is inconsistent with true understanding
and consciousness, then we would have to also conclude that the human brain
does not exhibit these qualities either. You can take John Searle’s Chinese room
argument and simply substitute “manipulating interneuronal connections and
synaptic strengths” for his words “manipulating symbols” and you will have a
convincing argument to the effect that human brains cannot truly understand
anything.

Another line of argument comes from the nature of nature, which has
become a new sacred ground for many observers. For example, New Zealand
biologist Michael Denton (born in 1943) sees a profound difference between the
design principles of machines and those of biology. Denton writes that natural
entities are “self-organizing,... self-referential,... self-replicating,...reciprocal,...
self-formative, and...holistic.”— He claims that such biological forms can only
be created through biological processes and that these forms are thereby
“immutable,...impenetrable, and...fundamental” realities of existence, and are
therefore basically a different philosophical category from machines.

The reality, as we have seen, is that machines can be designed using these
same principles. Learning the specific design paradigms of nature’s most
intelligent entity—the human brain—is precisely the purpose of the brain
reverse-engineering project. It is also not true that biological systems are
completely “holistic,” as Denton puts it, nor, conversely, do machines need to be
completely modular. We have clearly identified hierarchies of units of
functionality in natural systems, especially the brain, and AI systems are using
comparable methods.

It appears to me that many critics will not be satisfied until computers
routinely pass the Turing test, but even that threshold will not be clear-cut.
Undoubtedly, there will be controversy as to whether claimed Turing tests that
have been administered are valid. Indeed, I will probably be among those critics
disparaging early claims along these lines. By the time the arguments about the
validity of a computer passing the Turing test do settle down, computers will
have long since surpassed unenhanced human intelligence.

My emphasis here is on the word “unenhanced,” because enhancement is
precisely the reason that we are creating these “mind children,” as Hans
Moravec calls them.— Combining human-level pattern recognition with the
inherent speed and accuracy of computers will result in very powerful abilities.
But this is not an alien invasion of intelligent machines from Mars—we are
creating these tools to make ourselves smarter. I believe that most observers will
agree with me that this is what is unique about the human species: We build
these tools to extend our own reach.

EPILOGUE

The picture’s pretty bleak, gentlemen...The world’s climates are changing,
the mammals are taking over, and we all have a brain about the size of a
walnut.

—Dinosaurs talking, in The Far Side by Gary Larson

Intelligence may be defined as the ability to solve problems with limited
resources, in which a key such resource is time. Thus the ability to more quickly
solve a problem like finding food or avoiding a predator reflects greater power
of intellect. Intelligence evolved because it was useful for survival—a fact that
may seem obvious, but one with which not everyone agrees. As practiced by our
species, it has enabled us not only to dominate the planet but to steadily improve
the quality of our lives. This latter point, too, is not apparent to everyone, given
that there is a widespread perception today that life is only getting worse. For
example, a Gallup poll released on May 4, 2011, revealed that only “44 percent
of Americans believed that today’s youth will have a better life than their
parents.

If we look at the broad trends, not only has human life expectancy

quadrupled over the last millennium (and more than doubled in the last two

centuries),- but per capita GDP (in constant current dollars) has gone from

hundreds of dollars in 1800 to thousands of dollars today, with even more

pronounced trends in the developed world. 3 Only a handful of democracies
existed a century ago, whereas they are the norm today. For a historical
perspective on how far we have advanced, I suggest people read Thomas
Hobbes’s Leviathan (1651), in which he describes the “life of man” as “solitary,
poor, nasty, brutish, and short.” For a modern perspective, the recent book
Abundance (2012), by X-Prize Foundation founder (and cofounder with me of
Singularity University) Peter Diamandis and science writer Steven Kotler,

documents the extraordinary ways in which life today has steadily improved in
every dimension. Steven Pinker’s recent The Better Angels of Our Nature: Why
Violence Has Declined (2011) painstakingly documents the steady rise of
peaceful relations between people and peoples. American lawyer, entrepreneur,
and author Martine Rothblatt (born in 1954) documents the steady improvement
in civil rights, noting, for example, how in a couple of decades same-sex
marriage went from being legally recognized nowhere in the world to being
legally accepted in a rapidly growing number of jurisdictions.-

A primary reason that people believe that life is getting worse is because
our information about the problems of the world has steadily improved. If there
is a battle today somewhere on the planet, we experience it almost as if we were
there. During World War II, tens of thousands of people might perish in a battle,
and if the public could see it at all it was in a grainy newsreel in a movie theater
weeks later. During World War I a small elite could read about the progress of
the conflict in the newspaper (without pictures). During the nineteenth century
there was almost no access to news in a timely fashion for anyone.

The advancement we have made as a species due to our intelligence is
reflected in the evolution of our knowledge, which includes our technology and
our culture. Our various technologies are increasingly becoming information
technologies, which inherently continue to progress in an exponential manner. It
is through such technologies that we are able to address the grand challenges of
humanity, such as maintaining a healthy environment, providing the resources
for a growing population (including energy, food, and water), overcoming
disease, vastly extending human longevity, and eliminating poverty. It is only by
extending ourselves with intelligent technology that we can deal with the scale
of complexity needed to address these challenges.

These technologies are not the vanguard of an intelligent invasion that will
compete with and ultimately displace us. Ever since we picked up a stick to
reach a higher branch, we have used our tools to extend our reach, both
physically and mentally. That we can take a device out of our pocket today and
access much of human knowledge with a few keystrokes extends us beyond
anything imaginable by most observers only a few decades ago. The “cell
phone” (the term is placed in quotes because it is vastly more than a phone) in
my pocket is a million times less expensive yet thousands of times more
powerful than the computer all the students and professors at MIT shared when I
was an undergraduate there. That’s a several billion-fold increase in
price/performance over the last forty years, an escalation we will see again in the
next twenty-five years, when what used to fit in a building, and now fits in your
pocket, will fit inside a blood cell.

In this way we will merge with the intelligent technology we are creating.
Intelligent nanobots in our bloodstream will keep our biological bodies healthy
at the cellular and molecular levels. They will go into our brains noninvasively
through the capillaries and interact with our biological neurons, directly
extending our intelligence. This is not as futuristic as it may sound. There are
already blood cell-sized devices that can cure type I diabetes in animals or
detect and destroy cancer cells in the bloodstream. Based on the law of
accelerating returns, these technologies will be a billion times more powerful
within three decades than they are today.

I already consider the devices I use and the cloud of computing resources to
which they are virtually connected as extensions of myself, and feel less than
complete if I am cut off from these brain extenders. That is why the one-day
strike by Google, Wikipedia, and thousands of other Web sites against the SOPA
(Stop Online Piracy Act) on January 18, 2012, was so remarkable: I felt as if part
of my brain were going on strike (although I and others did find ways to access
these online resources). It was also an impressive demonstration of the political
power of these sites as the bill—which looked as if it was headed for ratification
—was instantly killed. But more important, it showed how thoroughly we have
already outsourced parts of our thinking to the cloud of computing. It is already
part of who we are. Once we routinely have intelligent nonbiological intelligence
in our brains, this augmentation—and the cloud it is connected to—will continue
to grow in capability exponentially.

The intelligence we will create from the reverse-engineering of the brain
will have access to its own source code and will be able to rapidly improve itself
in an accelerating iterative design cycle. Although there is considerable plasticity
in the biological human brain, as we have seen, it does have a relatively fixed
architecture, which cannot be significantly modified, as well as a limited
capacity. We are unable to increase its 300 million pattern recognizers to, say,
400 million unless we do so nonbiologically. Once we can achieve that, there
will be no reason to stop at a particular level of capability. We can go on to make
it a billion pattern recognizers, or a trillion.

From quantitative improvement comes qualitative advance. The most
important evolutionary advance in Homo sapiens was quantitative: the
development of a larger forehead to accommodate more neocortex. Greater
neocortical capacity enabled this new species to create and contemplate thoughts
at higher conceptual levels, resulting in the establishment of all the varied fields
of art and science. As we add more neocortex in a nonbiological form, we can
expect ever higher qualitative levels of abstraction.

British mathematician Irvin J. Good, a colleague of Alan Turing’s, wrote in

1965 that “the first ultraintelligent machine is the last invention that man need
ever make.” He defined such a machine as one that could surpass the
“intellectual activities of any man however clever” and concluded that “since the
design of machines is one of these intellectual activities, an ultraintelligent
machine could design even better machines; there would then unquestionably be
an 'intelligence explosion.’”

The last invention that biological evolution needed to make—the neocortex
—is inevitably leading to the last invention that humanity needs to make—truly
intelligent machines—and the design of one is inspiring the other. Biological
evolution is continuing but technological evolution is moving a million times
faster than the former. According to the law of accelerating returns, by the end of
this century we will be able to create computation at the limits of what is
possible, based on the laws of physics as applied to computation.- We call matter
and energy organized in this way “computronium,” which is vastly more
powerful pound per pound than the human brain. It will not just be raw
computation but will be infused with intelligent algorithms constituting all of
human-machine knowledge. Over time we will convert much of the mass and
energy in our tiny corner of the galaxy that is suitable for this purpose to
computronium. Then, to keep the law of accelerating returns going, we will need
to spread out to the rest of the galaxy and universe.

If the speed of light indeed remains an inexorable limit, then colonizing the
universe will take a long time, given that the nearest star system to Earth is four
light-years away. If there are even subtle means to circumvent this limit, our
intelligence and technology will be sufficiently powerful to exploit them. This is
one reason why the recent suggestion that the muons that traversed the 730
kilometers from the CERN accelerator on the Swiss-French border to the Gran
Sasso Laboratory in central Italy appeared to be moving faster than the speed of
light was such potentially significant news. This particular observation appears
to be a false alarm, but there are other possibilities to get around this limit. We
do not even need to exceed the speed of light if we can find shortcuts to other
apparently faraway places through spatial dimensions beyond the three with
which we are familiar. Whether we are able to surpass or otherwise get around
the speed of light as a limit will be the key strategic issue for the human-machine
civilization at the beginning of the twenty-second century.

Cosmologists argue about whether the world will end in fire (a big crunch
to match the big bang) or ice (the death of the stars as they spread out into an
eternal expansion), but this does not take into account the power of intelligence,
as if its emergence were just an entertaining sideshow to the grand celestial
mechanics that now rule the universe. How long will it take for us to spread our

intelligence in its nonbiological form throughout the universe? If we can
transcend the speed of light—admittedly a big if—for example, by using
wormholes through space (which are consistent with our current understanding
of physics), it could be achieved within a few centuries. Otherwise, it will likely
take much longer. In either scenario, waking up the universe, and then
intelligently deciding its fate by infusing it with our human intelligence in its
nonbiological form, is our destiny.

NOTES

Introduction

L Here is one sentence from One Hundred Years of Solitude by
Gabriel Garcia Marquez:

Aureliano Segundo was not aware of the singsong until the
following day after breakfast when he felt himself being bothered by a
buzzing that was by then more fluid and louder than the sound of the
rain, and it was Fernanda, who was walking throughout the house
complaining that they had raised her to be a queen only to have her
end up as a servant in a madhouse, with a lazy, idolatrous, libertine
husband who lay on his back waiting for bread to rain down from
heaven while she was straining her kidneys trying to keep afloat a
home held together with pins where there was so much to do, so much
to bear up under and repair from the time God gave his morning
sunlight until it was time to go to bed that when she got there her eyes
were full of ground glass, and yet no one ever said to her, “Good
morning, Fernanda, did you sleep well?,” nor had they asked her, even
out of courtesy, why she was so pale or why she awoke with purple
rings under her eyes in spite of the fact that she expected it, of course,
from a family that had always considered her a nuisance, an old rag, a
booby painted on the wall, and who were always going around saying
things against her behind her back, calling her churchmouse, calling
her Pharisee, calling her crafty, and even Amaranta, may she rest in
peace, had said aloud that she was one of those people who could not
tell their rectums from their ashes, God have mercy, such words, and
she had tolerated everything with resignation because of the Holy
Father, but she had not been able to tolerate it any more when that evil
Jose Arcadio Segundo said that the damnation of the family had come
when it opened its doors to a stuck-up highlander, just imagine, a
bossy highlander, Lord save us, a highlander daughter of evil spit of
the same stripe as the highlanders the government sent to kill workers,
you tell me, and he was referring to no one but her, the godchild of the
Duke of Alba, a lady of such lineage that she made the liver of
presidents’ wives quiver, a noble dame of fine blood like her, who had
the right to sign eleven peninsular names and who was the only mortal
creature in that town full of bastards who did not feel all confused at

the sight of sixteen pieces of silverware, so that her adulterous husband
could die of laughter afterward and say that so many knives and forks
and spoons were not meant for a human being but for a centipede, and
the only one who could tell with her eyes closed when the white wine
was served and on what side and in which glass and when the red wine
and on what side and in which glass and not like that peasant of an
Amaranta, may she rest in peace, who thought that white wine was
served in the daytime and red wine at night, and the only one on the
whole coast who could take pride in the fact that she took care of her
bodily needs only in golden chamberpots, so that Colonel Aureliano
Buendia, may he rest in peace, could have the effrontery to ask her
with his Masonic ill humor where she had received that privilege and
whether she did not shit shit but shat sweet basil, just imagine, with
those very words, and so that Renata, her own daughter, who through
an oversight had seen her stool in the bedroom, had answered that
even if the pot was all gold and with a coat of arms, what was inside
was pure shit, physical shit, and worse even than any other kind
because it was stuck-up highland shit, just imagine, her own daughter,
so that she never had any illusions about the rest of the family, but in
any case she had the right to expect a little more consideration from
her husband because, for better or for worse, he was her consecrated
spouse, her helpmate, her legal despoiler, who took upon himself of
his own free and sovereign will the grave responsibility of taking her
away from her paternal home, where she never wanted for or suffered
from anything, where she wove funeral wreaths as a pastime, since her
godfather had sent a letter with his signature and the stamp of his ring
on the sealing wax simply to say that the hands of his goddaughter
were not meant for tasks of this world except to play the clavichord,
and, nevertheless, her insane husband had taken her from her home
with all manner of admonitions and warnings and had brought her to
that frying pan of hell where a person could not breathe because of the
heat, and before she had completed her Pentecostal fast he had gone
off with his wandering trunks and his wastrel’s accordion to loaf in
adultery with a wretch of whom it was only enough to see her behind,
well, that’s been said, to see her wiggle her mare’s behind in order to
guess that she was a, that she was a, just the opposite of her, who was a
lady in a palace or a pigsty, at the table or in bed, a lady of breeding,
God-fearing, obeying His laws and submissive to His wishes, and with
whom he could not perform, naturally, the acrobatics and trampish

antics that he did with the other one, who, of course, was ready for
anything, like the French matrons, and even worse, if one considers
well, because they at least had the honesty to put a red light at their
door, swinishness like that, just imagine, and that was all that was
needed by the only and beloved daughter of Dona Renata Argote and
Don Fernando del Carpio, and especially the latter, an upright man, a
fine Christian, a Knight of the Order of the Holy Sepulcher, those who
receive direct from God the privilege of remaining intact in their
graves with their skin smooth like the cheeks of a bride and their eyes
alive and clear like emeralds.

Z See the graph “Growth in Genbank DNA Sequence Data” in chapter

10 .

Z Cheng Zhang and Jianpeng Ma, “Enhanced Sampling and
Applications in Protein Folding in Explicit Solvent,” Journal of
Chemical Physics 132, no. 24 (2010): 244101. See also

http://folding.stanford.edu/English/About about the Folding@home
project, which has harnessed over five million computers around the
world to simulate protein folding.

4 For a more complete description of this argument, see the section
“[The Impact...] on the Intelligent Destiny of the Cosmos: Why We
Are Probably Alone in the Universe” in chapter 6 of The Singularity
Is Near by Ray Kurzweil (New York: Viking, 2005).

5, James D. Watson, Discovering the Brain (Washington, DC: National
Academies Press, 1992).

£L Sebastian Seung, Connectome: How the Brain’s Wiring Makes Us
Who We Are (New York: Houghton Mifflin Harcourt, 2012).

Z “Mandelbrot Zoom,” http://www.youtube.com/watch?
v=gEw8xpblaRA; “Fractal Zoom Mandelbrot Corner,”
http://www. youtube.com/watch?v=G_GBwuYuOOs.

Chapter 1: Thought Experiments on the World

L Charles Darwin, The Origin of Species (P. F. Collier & Son, 1909),
185/95-96.

Z Darwin, On the Origin of Species, 751 (206.1.1-6), Peckham’s
Variorum edition, edited by Morse Peckham, The Origin of Species
by Charles Darwin: A Variorum Text (Philadelphia: University of
Pennsylvania Press, 1959).

Z R. Dahm, “Discovering DNA: Friedrich Miescher and the Early
Years of Nucleic Acid Research,” Human Genetics 122, no. 6
(2008): 565-81, doi:10.1007/s00439-007-0433-0; PMID 17901982.

4 Valery N. Soyfer, “The Consequences of Political Dictatorship for
Russian Science,” Nature Reviews Genetics 2, no. 9 (2001): 723-29,
doi: 10.1038/35088598; PMID 11533721.

5, J. D. Watson and F. H. C. Crick, “A Structure for Deoxyribose
Nucleic Acid,” Nature 171 (1953): 737-38,

http://www.nature.com/nature/dna50/watsoncrick.pdf and “Double
Helix: 50 Years of DNA,” Nature archive,

http://www.nature.com/nature/dna50/archive.xhtml.

(k Franklin died in 1958 and the Nobel Prize for the discovery of DNA
was awarded in 1962. There is controversy as to whether or not she
would have shared in that prize had she been alive in 1962.

Z Albert Einstein, “On the Electrodynamics of Moving Bodies”
(1905). This paper established the special theory of relativity. See
Robert Bruce Lindsay and Henry Margenau, Foundations of Physics
(Woodbridge, CT: Ox Bow Press, 1981), 330.

8, “Crookes radiometer,” Wikipedia,

http://en.wikipedia.org/wiki/Crookes_radiometer.

9* Note that some of the momentum of the photons is transferred to the
air molecules in the bulb (since it is not a perfect vacuum) and then
transferred from the heated air molecules to the vane.

10. Albert Einstein, “Does the Inertia of a Body Depend Upon Its
Energy Content?” (1905). This paper established Einstein’s
famous formula E = me 2 .

11. “Albert Einstein’s Letters to President Franklin Delano Roosevelt,”

http://hypertextbook.com/eworld/einstein.shtml.

Chapter 3: A Model of the Neocortex: The
Pattern Recognition Theory of Mind

E Some nonmammals, such as crows, parrots, and octopi, are reported
to be capable of some level of reasoning; however, this is limited
and has not been sufficient to create tools that have their own
evolutionary course of development. These animals may have
adapted other brain regions to perform a small number of levels of
hierarchical thinking, but a neocortex is required for the relatively
unrestricted hierarchical thinking that humans can perform.

T V. B. Mountcastle, “An Organizing Principle for Cerebral Function:
The Unit Model and the Distributed System” (1978), in Gerald M.
Edelman and Vernon B. Mountcastle, The Mindful Brain: Cordcal
Organization and the Group-Selective Theory of Higher Brain
Function (Cambridge, MA: MIT Press, 1982).

T Herbert A. Simon, “The Organization of Complex Systems,” in
Howard H. Pattee, ed., Hierarchy Theory: The Challenge of
Complex Systems (New York: George Braziller, Inc., 1973),
http://blog.santafe.edu/wp-content/uploads/2009/03/simon 1973.pdf.
4 Marc D. Hauser, Noam Chomsky, and W. Tecumseh Fitch, “The
Faculty of Language: What Is It, Who Has It, and How Did It
Evolve?” Science 298 (November 2002): 1569-79,

http://www.sciencemag.org/content/298/5598/1569.short.

5, The following passage from the book Transcend: Nine Steps to
Living Well Forever, by Ray Kurzweil and Terry Grossman (New
York: Rodale, 2009), describes this lucid dreaming technique in
more detail:

I’ve developed a method of solving problems while I sleep. I’ve
perfected it for myself over several decades and have learned the
subtle means by which this is likely to work better.

I start out by assigning myself a problem when I get into bed.
This can be any kind of problem. It could be a math problem, an issue
with one of my inventions, a business strategy question, or even an
interpersonal problem.

I’ll think about the problem for a few minutes, but I try not to

solve it. That would just cut off the creative problem solving to come. I
do try to think about it. What do I know about this? What form could a
solution take? And then I go to sleep. Doing this primes my
subconscious mind to work on the problem.

Terry: Sigmund Freud pointed out that when we dream, many of
the censors in our brain are relaxed, so that we might dream about
things that are socially, culturally, or even sexually taboo. We can
dream about weird things that we wouldn’t allow ourselves to think
about during the day. That’s at least one reason why dreams are
strange.

Ray: There are also professional blinders that prevent people from
thinking creatively, many of which come from our professional
training, mental blocks such as “you can’t solve a signal processing
problem that way” or “linguistics is not supposed to use those rules.”
These mental assumptions are also relaxed in our dream state, so I’ll
dream about new ways of solving problems without being burdened by
these daytime constraints.

Terry: There’s another part of our brain also not working when we
dream, our rational faculties to evaluate whether an idea is reasonable.
So that’s another reason that weird or fantastic things happen in our
dreams. When the elephant walks through the wall, we aren’t shocked
as to how the elephant could do this. We just say to our dream selves,
“Okay, an elephant walked through the wall, no big deal.” Indeed, if I
wake up in the middle of the night, I often find that I’ve been
dreaming in strange and oblique ways about the problem that I
assigned myself.

Ray: The next step occurs in the morning in the halfway state
between dreaming and being awake, which is often called lucid
dreaming. In this state, I still have the feelings and imagery from my
dreams, but now I do have my rational faculties. I realize, for example,
that I am in a bed. And I could formulate the rational thought that I
have a lot to do so I had better get out of bed. But that would be a
mistake. Whenever I can, I will stay in bed and continue in this lucid
dream state because that is key to this creative problem-solving
method. By the way, this doesn’t work if the alarm rings.

Reader: Sounds like the best of both worlds.

Ray: Exactly. I still have access to the dream thoughts about the
problem I assigned myself the night before. But now I’m sufficiently
conscious and rational to evaluate the new creative ideas that came to
me during the night. I can determine which ones make sense. After
perhaps 20 minutes of this, I invariably will have keen new insights
into the problem.

I’ve come up with inventions this way (and spent the rest of the
day writing a patent application), figured out how to organize material
for a book such as this, and come up with useful ideas for a diverse set
of problems. If I have a key decision to make, I will always go through
this process, after which I am likely to have real confidence in my
decision.

The key to the process is to let your mind go, to be nonjudgmental,
and not to worry about how well the method is working. It is the
opposite of a mental discipline. Think about the problem, but then let
ideas wash over you as you fall asleep. Then in the morning, let your
mind go again as you review the strange ideas that your dreams
generated. I have found this to be an invaluable method for harnessing
the natural creativity of my dreams.

Reader: Well, for the workaholics among us, we can now work in
our dreams. Not sure my spouse is going to appreciate this.

Ray: Actually, you can think of it as getting your dreams to do
your work for you.

Chapter 4: The Biological Neocortex

L Steven Pinker, How the Mind Works (New York: Norton, 1997),
152-53.

2, D. O. Hebb, The Organization of Behavior (New York: John Wiley
& Sons, 1949).

Z Henry Markram and Rodrigo Perrin, “Innate Neural Assemblies for
Lego Memory,” Frontiers in Neural Circuits 5, no. 6 (2011).

4 E-mail communication from Henry Markram, February 19, 2012.

5, Van J. Wedeen et al., “The Geometric Structure of the Brain Fiber
Pathways,” Science 335, no. 6076 (March 30, 2012).

£L Tai Sing Lee, “Computations in the Early Visual Cortex,” Journal of
Physiology—Paris 97 (2003): 121-39.

Z A list of papers can be found at
http://cbcl.mit.edu/people/poggio/tpcv_short_pubs.pdf.

8, Daniel J. Felleman and David C. Van Essen, “Distributed
Hierarchical Processing in the Primate Cerebral Cortex,” Cerebral
Cortex 1, no. 1 (January/February 1991): 1-47. A compelling
analysis of the Bayesian mathematics of the top-down and bottom-
up communication in the neocortex is provided by Tai Sing Lee in
“Hierarchical Bayesian Inference in the Visual Cortex,” Journal of
the Optical Society of America 20, no. 7 (July 2003): 1434-48.

9* Uri Hasson et al., “A Hierarchy of Temporal Receptive Windows in
Human Cortex,” Journal of Neuroscience 28, no. 10 (March 5,
2008): 2539-50.

10. Marina Bedny et al., “Language Processing in the Occipital Cortex

of Congenitally Blind Adults,” Proceedings of the National
Academy of Sciences 108, no. 11 (March 15, 2011): 4429-34.

11. Daniel E. Feldman, “Synaptic Mechanisms for Plasticity in
Neocortex,” Annual Review of Neuroscience 32 (2009): 33-55.

12. Aaron C. Koralek et al., “Corticostriatal Plasticity Is Necessary for

Learning Intentional Neuroprosthetic Skills,” Nature 483 (March
15, 2012): 331-35.

13. E-mail communication from Randal Koene, January 2012.

14. Min Fu, Xinzhu Yu, Ju Lu, and Yi Zuo, “Repetitive Motor
Learning Induces Coordinated Formation of Clustered Dendritic

Spines in Vivo,” Nature 483 (March 1, 2012): 92-95.

15. Dario Bonanomi et al., “Ret Is a Multifunctional Coreceptor That

Integrates Diffusible- and Contact-Axon Guidance Signals,” Cell
148, no. 3 (February 2012): 568-82.

16, See endnote 7 in chapter 11 .

Chapter 5: The Old Brain

L Vernon B. Mountcastle, “The View from Within: Pathways to the
Study of Perception,” Johns Hopkins Medical Journal 136 (1975):
109-31.

Z B. Roska and F. Werblin, “Vertical Interactions Across Ten Parallel,
Stacked Representations in the Mammalian Retina,” Nature 410, no.
6828 (March 29, 2001): 583-87; “Eye Strips Images of All but Bare
Essentials Before Sending Visual Information to Brain, UC
Berkeley Research Shows,” University of California at Berkeley
news release, March 28, 2001,

www.berkeley.edu/news/media/releases/2001/03/28_wersl.xhtml.

3, Lloyd Watts, “Reverse-Engineering the Human Auditory Pathway,”
in J. Liu et al., eds., WCCI 2012 (Berlin: Springer-Verlag, 2012),
47-59. Lloyd Watts, “Real-Time, High-Resolution Simulation of the
Auditory Pathway, with Application to Cell-Phone Noise

Reduction,” ISCAS (June 2, 2010): 3821-24. For other papers see
http://www.lloydwatts.com/publications.xhtml.

4 See Sandra Blakeslee, “Humanity? Maybe It’s All in the Wiring,”
New York Times, December 11, 2003,

http://www.nytimes.com/2003/12/09/science/09BRAI.xhtml.

5, T. E. J. Behrens et al., “Non-Invasive Mapping of Connections
between Human Thalamus and Cortex Using Diffusion Imaging,”
Nature Neuroscience 6, no. 7 (July 2003): 750-57.

6, Timothy J. Buschman et al., “Neural Substrates of Cognitive

Capacity Limitations,” Proceedings of the National Academy of
Sciences 108, no. 27 (July 5, 2011): 11252-55,

http://www.pnas.org/content/108/27/11252.long.

Z Theodore W. Berger et al., “A Cortical Neural Prosthesis for
Restoring and Enhancing Memory,” Journal of Neural Enqineerinq
8, no. 4 (August 2011).

8, Basis functions are nonlinear functions that can be combined
linearly (by adding together multiple weighted-basis functions) to
approximate any nonlinear function. A. Pouget and L. H. Snyder,
“Computational Approaches to Sensorimotor Transformations,”
Nature Neuroscience 3, no. 11 Supplement (November 2000):

1192-98.

9* J. R. Bloedel, “Functional Heterogeneity with Structural
Homogeneity: How Does the Cerebellum Operate?” Behavioral and
Brain Sciences 15, no. 4 (1992): 666-78.

10. S. Grossberg and R. W. Paine, “A Neural Model of Cortico-
Cerebellar Interactions during Attentive Imitation and Predictive
Learning of Sequential Handwriting Movements,” Neural
Networks 13, no. 8-9 (October-November 2000): 999-1046.

11. Javier F. Medina and Michael D. Mauk, “Computer Simulation of

Cerebellar Information Processing,” Nature Neuroscience 3
(November 2000): 1205-11.

12. James Olds, “Pleasure Centers in the Brain,” Scientific American

(October 1956): 105-16. Aryeh Routtenberg, “The Reward
System of the Brain,” Scientific American 239 (November 1978):
154-64. K. C. Berridge and M. L. Kringelbach, “Affective
Neuroscience of Pleasure: Reward in Humans and Other
Animals,” Psychopharmacology 199 (2008): 457-80. Morten L.
Kringelbach, The Pleasure Center: Trust Your Animal Instincts
(New York: Oxford University Press, 2009). Michael R.
Liebowitz, The Chemistry of Love (Boston: Little, Brown, 1983).
W. L. Witters and P. Jones-Witters, Human Sexuality: A
Biological Perspective (New York: Van Nostrand, 1980).

Chapter 6: Transcendent Abilities

L Michael Nielsen, Reinventing Discovery: The New Era of
Networked Science (Princeton, NJ: Princeton University Press,
2012), 1-3. T. Gowers and M. Nielsen, “Massively Collaborative
Mathematics,” Nature 461, no. 7266 (2009): 879-81. “A

Combinatorial Approach to Density Hales-Jewett,” Gowers’s
Weblog, http://gowers.wordpress.eom/2009/02/01/a-combinatorial-
approach-to-density-hales-jewett/. Michael Nielsen, “The Polymath
Project: Scope of Participation,” March 20, 2009,

http://michaelnielsen.org/blog/?p=584. Julie Rehmeyer, “SIAM:
Massively Collaborative Mathematics,” Society for Industrial and
Applied Mathematics, April 1, 2010,

http ://www. siam.org/news/news .php?id=1731.

T P. Dayan and Q. J. M. Huys, “Serotonin, Inhibition, and Negative
Mood,” PLoS Computational Biology 4, no. 1 (2008),

http://compbiol.plosjournals.org/perlserv/?request=get-
document&doi=10.1371/journal.pcbi.0040004.

[CO ["vl pi

Chapter 7: The Biologically Inspired Digital
Neocortex

L Gary Cziko, Without Miracles: Universal Selection Theory and the
Second Darwinian Revolution (Cambridge, MA: MIT Press, 1955).

2, David Dalrymple has been a mentee of mine since he was eight
years old in 1999. You can read his background here:
http://esp.mit.edu/learn/teachers/davidad/bio.xhtml, and

http://www.brainsciences.org/Research-Team/mr-david-
dalrymple.xhtml.

T Jonathan Fildes, “Artificial Brain ‘10 Years Away,”’ BBC News,
July 22, 2009, http://news.bbc.co.Uk/2/hF8164060.stm. See also the
video “Henry Markram on Simulating the Brain: The Next Decisive
Years,” http://www.kurzweilai.net/henry-markram-simulating-the-
brain-next-decisive-years.

4 M. Mitchell Waldrop, “Computer Modelling: Brain in a Box,”
Nature News, February 22, 2012,

http://www.nature.com/news/computer-modelling-brain-in-a-box-
1.10066.

5, Jonah Lehrer, “Can a Thinking, Remembering, Decision-Making
Biologically Accurate Brain Be Built from a Supercomputer?” Seed,
http://seedmagazine.com/content/article/out_of_the_blue/.

Fildes, “Artificial Brain ‘10 Years Away.’”

See http://www.humanconnectomeproject.org/.

Anders Sandberg and Nick Bostrom, Whole Brain Emulation: A
Roadmap, Technical Report #2008-3 (2008), Future of Humanity
Institute, Oxford University, www.fhi.ox.ac.uk/reports/2008-3.pdf.

9* Here is the basic schema for a neural net algorithm. Many variations
are possible, and the designer of the system needs to provide certain
critical parameters and methods, detailed on the following pages.

Creating a neural net solution to a problem involves the following
steps:

Define the input.

Define the topology of the neural net (i.e., the layers of neurons

and the connections between the neurons).

Train the neural net on examples of the problem.

Run the trained neural net to solve new examples of the problem.
Take your neural net company public.

These steps (except for the last one) are detailed below:

The Problem Input

The problem input to the neural net consists of a series of numbers.
This input can be:

In a visual pattern recognition system, a two-dimensional array of
numbers representing the pixels of an image; or

In an auditory (e.g., speech) recognition system, a two-dimensional
array of numbers representing a sound, in which the first dimension
represents parameters of the sound (e.g., frequency components) and
the second dimension represents different points in time; or

In an arbitrary pattern recognition system, an n-dimensional array
of numbers representing the input pattern.

Defining the Topology

To set up the neural net, the architecture of each neuron consists of:

Multiple inputs in which each input is “connected” to either the
output of another neuron or one of the input numbers.

Generally, a single output, which is connected to either the input of
another neuron (which is usually in a higher layer) or the final output.

Set Up the First Layer of Neurons

Create N 0 neurons in the first layer. For each of these neurons,
“connect” each of the multiple inputs of the neuron to “points” (i.e.,
numbers) in the problem input. These connections can be determined
randomly or using an evolutionary algorithm (see below).

Assign an initial “synaptic strength” to each connection created.
These weights can start out all the same, can be assigned randomly, or
can be determined in another way (see below).

Set Up the Additional Layers of Neurons

Set up a total of M layers of neurons. For each layer, set up the neurons
in that layer.

For layerp

Create N [ neurons in layer^ For each of these neurons, “connect”
each of the multiple inputs of the neuron to the outputs of the neurons
in layer,., (see variations below).

Assign an initial “synaptic strength” to each connection created.
These weights can start out all the same, can be assigned randomly, or
can be determined in another way (see below).

The outputs of the neurons in layer M are the outputs of the neural
net (see variations below).

The Recognition Trials
How Each Neuron Works

Once the neuron is set up, it does the following for each
recognition trial:

Each weighted input to the neuron is computed by multiplying the
output of the other neuron (or initial input) that the input to this neuron
is connected to by the synaptic strength of that connection.

All of these weighted inputs to the neuron are summed.

If this sum is greater than the firing threshold of this neuron, then
this neuron is considered to fire and its output is 1. Otherwise, its
output is 0 (see variations below).

Do the Following for Each Recognition Trial

For each layer, from layer 0 to layer M :

For each neuron in the layer:

Sum its weighted inputs (each weighted input = the output of the
other neuron [or initial input] that the input to this neuron is connected
to, multiplied by the synaptic strength of that connection).

If this sum of weighted inputs is greater than the firing threshold
for this neuron, set the output of this neuron = 1, otherwise set it to 0.

To Train the Neural Net

Run repeated recognition trials on sample problems.

After each trial, adjust the synaptic strengths of all the interneuronal
connections to improve the performance of the neural net on this trial (see
the discussion below on how to do this).

Continue this training until the accuracy rate of the neural net is no
longer improving (i.e., reaches an asymptote).

Key Design Decisions

In the simple schema above, the designer of this neural net algorithm needs
to determine at the outset:

What the input numbers represent.

The number of layers of neurons.

The number of neurons in each layer. (Each layer does not necessarily
need to have the same number of neurons.)

The number of inputs to each neuron in each layer. The number of
inputs (i.e., interneuronal connections) can also vary from neuron to neuron
and from layer to layer.

The actual “wiring” (i.e., the connections). For each neuron in each
layer, this consists of a list of other neurons, the outputs of which constitute
the inputs to this neuron. This represents a key design area. There are a
number of possible ways to do this:

(1) Wire the neural net randomly; or

(2) Use an evolutionary algorithm (see below) to determine an optimal

wiring; or

(3) Use the system designer’s best judgment in determining the wiring.

The initial synaptic strengths (i.e., weights) of each connection. There
are a number of possible ways to do this:

(1) Set the synaptic strengths to the same value; or

(2) Set the synaptic strengths to different random values; or

(3) Use an evolutionary algorithm to determine an optimal set of initial

values; or

(4) Use the system designer’s best judgment in determining the initial

values.

The firing threshold of each neuron.

Determine the output. The output can be:

(1) the outputs of layer M of neurons; or

(2) the output of a single output neuron, the inputs of which are the outputs

of the neurons in layer M ; or

(3) a function of (e.g., a sum of) the outputs of the neurons in layer M ; or

(4) another function of neuron outputs in multiple layers.

Determine how the synaptic strengths of all the connections are adjusted
during the training of this neural net. This is a key design decision and is
the subject of a great deal of research and discussion. There are a number of
possible ways to do this:

(1) For each recognition trial, increment or decrement each synaptic
strength by a (generally small) fixed amount so that the neural net’s
output more closely matches the correct answer. One way to do this is
to try both incrementing and decrementing and see which has the more
desirable effect. This can be time-consuming, so other methods exist
for making local decisions on whether to increment or decrement each
synaptic strength.

(2) Other statistical methods exist for modifying the synaptic strengths after
each recognition trial so that the performance of the neural net on that
trial more closely matches the correct answer.

Note that neural net training will work even if the answers to the
training trials are not all correct. This allows using real-world training data
that may have an inherent error rate. One key to the success of a neural net-
based recognition system is the amount of data used for training. Usually a
very substantial amount is needed to obtain satisfactory results. As with
human students, the amount of time that a neural net spends learning its
lessons is a key factor in its performance.

Variations

Many variations of the above are feasible. For example:

There are different ways of determining the topology. In particular, the
interneuronal wiring can be set either randomly or using an evolutionary
algorithm.

There are different ways of setting the initial synaptic strengths.

The inputs to the neurons in laye^ do not necessarily need to come from
the outputs of the neurons in layer,.]. Alternatively, the inputs to the
neurons in each layer can come from any lower layer or any layer.

There are different ways to determine the final output.

The method described above results in an “all or nothing” (1 or 0) firing
called a nonlinearity. There are other nonlinear functions that can be used.
Commonly a function is used that goes from 0 to 1 in a rapid but more
gradual fashion. Also, the outputs can be numbers other than 0 and 1.

The different methods for adjusting the synaptic strengths during
training represent key design decisions.

The above schema describes a “synchronous” neural net, in which each
recognition trial proceeds by computing the outputs of each layer, starting
with layer 0 through layer M . In a true parallel system, in which each neuron

is operating independently of the others, the neurons can operate
“asynchronously” (i.e., independently). In an asynchronous approach, each
neuron is constantly scanning its inputs and fires whenever the sum of its
weighted inputs exceeds its threshold (or whatever its output function
specifies).

10. Robert Mannell, “Acoustic Representations of Speech,” 2008,
http://clas.mq.edu.au/acoustics/frequency/acoustic_speech.xhtml.

11. Here is the basic schema for a genetic (evolutionary) algorithm. Many

variations are possible, and the designer of the system needs to provide
certain critical parameters and methods, detailed below.

The Evolutionary Algorithm

Create N solution “creatures.” Each one has:

A genetic code: a sequence of numbers that characterize a possible
solution to the problem. The numbers can represent critical parameters,
steps to a solution, rules, etc.

For each generation of evolution, do the following:

Do the following for each of the N solution creatures:

Apply this solution creature’s solution (as represented by its genetic
code) to the problem, or simulated environment. Rate the solution.

Pick the L solution creatures with the highest ratings to survive into the
next generation.

Eliminate the (N - L) nonsurviving solution creatures.

Create (N - L ) new solution creatures from the L surviving solution
creatures by:

(1) Making copies of the L surviving creatures. Introduce small random

variations into each copy; or

(2) Create additional solution creatures by combining parts of the genetic

code (using “sexual” reproduction, or otherwise combining portions of
the chromosomes) from the L surviving creatures; or

(3) Do a combination of (1) and (2).

Determine whether or not to continue evolving:

Improvement = (highest rating in this generation) - (highest rating in
the previous generation).

If Improvement < Improvement Threshold then we’re done.

The solution creature with the highest rating from the last generation of

evolution has the best solution. Apply the solution defined by its genetic
code to the problem.

Key Design Decisions

In the simple schema above, the designer needs to determine at the outset:
Key parameters:

N

L

Improvement threshold.

What the numbers in the genetic code represent and how the solution is
computed from the genetic code.

A method for determining the N solution creatures in the first
generation. In general, these need only be “reasonable” attempts at a
solution. If these first-generation solutions are too far afield, the
evolutionary algorithm may have difficulty converging on a good solution.
It is often worthwhile to create the initial solution creatures in such a way
that they are reasonably diverse. This will help prevent the evolutionary
process from just finding a “locally” optimal solution.

How the solutions are rated.

How the surviving solution creatures reproduce.

Variations

Many variations of the above are feasible. For example:

There does not need to be a fixed number of surviving solution
creatures (L) from each generation. The survival rule(s) can allow for a
variable number of survivors.

There does not need to be a fixed number of new solution creatures
created in each generation (N - L). The procreation rules can be
independent of the size of the population. Procreation can be related to
survival, thereby allowing the fittest solution creatures to procreate the
most.

The decision as to whether or not to continue evolving can be varied. It
can consider more than just the highest-rated solution creature from the
most recent generation(s). It can also consider a trend that goes beyond just
the last two generations.

12. Dileep George, “How the Brain Might Work: A Hierarchical and
Temporal Model for Learning and Recognition” (PhD dissertation,
Stanford University, June 2008).

13. A. M. Turing, “Computing Machinery and Intelligence,” Mind, October

1950.

14. Hugh Loebner has a “Loebner Prize” competition that is run each year.

The Loebner silver medal will go to a computer that passes Turing’s
original text-only test. The gold medal will go to a computer that can
pass a version of the test that includes audio and video input and
output. In my view, the inclusion of audio and video does not actually
make the test more challenging.

15. “Cognitive Assistant That Learns and Organizes,” Artificial Intelligence

Center, SRI International, http://www.ai.sri.com/project/CALO.

16. Dragon Go! Nuance Communications, Inc.,
http://www.nuance.com/products/dragon-go-in-action/index.htm.

17. “Overcoming Artificial Stupidity,” WolframAlpha Blog, April 17, 2012,

http://blog.wolframalpha.com/author/stephenwolfram/.

Chapter 8: The Mind as Computer

L Salomon Bochner, A Biographical Memoir of John von Neumann
(Washington, DC: National Academy of Sciences, 1958).

Z A. M. Turing, “On Computable Numbers, with an Application to the
Entscheidungsproblem,” Proceedings of the London Mathematical
Society Series 2, vol. 42 (1936-37): 230-65,

http://www.comlab.ox.ac.uk/activities/ieg/e-library/sources/tp2-
ie.pdf. A. M. Turing, “On Computable Numbers, with an
Application to the Entscheidungsproblem: A Correction,”
Proceedings of the London Mathematical Society 43 (1938): 544-
46.

Z John von Neumann, “First Draft of a Report on the ED VAC,”
Moore School of Electrical Engineering, University of
Pennsylvania, June 30, 1945. John von Neumann, “A Mathematical
Theory of Communication,” Bell System Technical Journal, July
and October 1948.

4 Jeremy Bernstein, The Analytical Engine: Computers — Past,
Present, and Future, rev. ed. (New York: William Morrow & Co.,
1981).

5, “Japan’s K Computer Tops 10 Petaflop/s to Stay Atop TOP500
List,” Top 500, November 11, 2011,

http://top500.org/lists/2011/ll/press-release.

£L Carver Mead, Analog VLSI and Neural Systems (Reading, MA:
Addison-Wesley, 1986).

Z “IBM Unveils Cognitive Computing Chips,” IBM news release,
August 18, 2011, http://www-

03.ibm.com/press/us/en/pressrelease/35251.wss.

8, “Japan’s K Computer Tops 10 Petaflop/s to Stay Atop TOP500
List.”

Chapter 9: Thought Experiments on the Mind

L John R. Searle, “I Married a Computer,” in Jay W. Richards, ed.,
Are We Spiritual Machines? Ray Kurzweil vs. the Critics of Strong
AI (Seattle: Discovery Institute, 2002).

Z Stuart Hameroff, Ultimate Computing: Biomolecular Consciousness
and Nanotechnology (Amsterdam: Elsevier Science, 1987).

Z R S. Sebel et al., “The Incidence of Awareness during Anesthesia: A
Multicenter United States Study,” Anesthesia and Analgesia 99
(2004): 833-39.

4 Stuart Sutherland, The International Dictionary of Psychology (New
York: Macmillan, 1990).

5, David Cockburn, “Human Beings and Giant Squids,” Philosophy
69, no. 268 (April 1994): 135-50.

(k Ivan Petrovich Pavlov, from a lecture given in 1913, published in
Lectures on Conditioned Reflexes: Twenty-Five Years of Objective
Study of the Higher Nervous Activity [Behavior] of Animals
(London: Martin Lawrence, 1928), 222.

Z Roger W. Sperry, from James Arthur Lecture on the Evolution of the
Human Brain, 1964, p. 2.

8, Henry Maudsley, “The Double Brain,” Mind 14, no. 54 (1889):
161-87.

9* Susan Curtiss and Stella de Bode, “Language after
Hemispherectomy,” Brain and Cognition 43, nos. 1-3 (June-August
2000): 135-38.

10. E. P. Vining et al., “Why Would You Remove Half a Brain? The

Outcome of 58 Children after Hemispherectomy—the Johns
Hopkins Experience: 1968 to 1996,” Pediatrics 100 (August
1997): 163-71. M. B. Pulsifer et al., “The Cognitive Outcome of
Hemispherectomy in 71 Children,” Epilepsia 45, no. 3 (March
2004): 243-54.

11. S. McClelland III and R. E. Maxwell, “Hemispherectomy for
Intractable Epilepsy in Adults: The First Reported Series,” Annals
of Neurology 61, no. 4 (April 2007): 372-76.

12. Lars Muckli, Marcus J. Naumerd, and Wolf Singer, “Bilateral
Visual Field Maps in a Patient with Only One Hemisphere,”

Proceedings of the National Academy of Sciences 106, no. 31
(August 4, 2009), http://dx.doi.org/10.1073/pnas.0809688106.

13. Marvin Minsky, The Society of Mind (New York: Simon and
Schuster, 1988).

14. F. Fay Evans-Martin, The Nervous System (New York: Chelsea
House, 2005), http://www.scribd.com/doc/5012597/The-Nervous-
System.

15. Benjamin Libet, Mind Time: The Temporal Factor in
Consciousness (Cambridge, MA: Harvard University Press,
2005).

16. Daniel C. Dennett, Freedom Evolves (New York: Viking, 2003).

17. Michael S. Gazzaniga, Who’s in Charge? Free Will and the Science

of the Brain (New York: Ecco/HarperCollins, 2011).

18. David Hume, An Enquiry Concerning Human Understanding
(1765), 2nd ed., edited by Eric Steinberg (Indianapolis: Hackett,
1993).

19. Arthur Schopenhauer, The Wisdom of Life.

20. Arthur Schopenhauer, On the Freedom of the Will (1839).

21. From Raymond Smullyan, 5000 B.C. and Other Philosophical
Fantasies (New York: St. Martin’s Press, 1983).

22. For an insightful and entertaining examination of similar issues of

identity and consciousness, see Martine Rothblatt, “The Terasem
Mind Uploading Experiment,” International Journal of Machine
Consciousness 4, no. 1 (2012): 141-58. In this paper, Rothblatt
examines the issue of identity with regard to software that
emulates a person based on “a database of video interviews and
associated information about a predecessor person.” In this
proposed future experiment, the software is successfully
emulating the person it is based on.

23. “How Do You Persist When Your Molecules Don’t?” Science and

Consciousness Review 1, no. 1 (June 2004), http://www.sci-
con.org/articles/20040601.xhtml.

IN IP

Chapter 10: The Law of Accelerating Returns
Applied to the Brain

L “DNA Sequencing Costs,” National Human Genome Research
Institute, NIH, http://www.genome.gov/sequencingcosts/.

2, “Genetic Sequence Data Bank, Distribution Release Notes,”
December 15, 2009, National Center for Biotechnology

Information, National Library of Medicine,
ftp://ftp.ncbi.nih.gov/genbank/gbrel.txt.

T “DNA Sequencing—The History of DNA Sequencing,” January 2,
2012, http://www.dnasequencing.org/history-of-dna.

4 “Cooper’s Law,” ArrayComm,

http://www.arraycomm.com/technology/coopers-law.

5, “The Zettabyte Era,” Cisco,

http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns53Z
and “Number of Internet Hosts,” Internet Systems Consortium,
http://www.isc.org/solutions/survey/history.

TeleGeography © PriMetrica, Inc., 2012.

Dave Kristula, “The History of the Internet” (March 1997, update
August 2001), http://www.davesite.com/webstation/net-
history.shtml; Robert Zakon, “Hobbes’ Internet Timeline v8.0,”
http://www.zakon.org/robert/internet/timeline; Quest

Communications, 8-K for 9/13/1998 EX-99.1; Converge! Network
Digest, December 5, 2002,

http ://www. convergedige st.com/Daily/daily. asp?
vn=v9n229&fecha=December%2005,%202002; Jim Duffy, “AT&T
Plans Backbone Upgrade to 40G,” Computerworld, June 7, 2006,
http://www.computerworld.com/action/article.do?
command=viewArticleBasic&articleId=9001032; “40G: The Fastest
Connection You Can Get?” InternetNews.com, November 2, 2007,
http ://www. internetnews. com/inf r a/article .php/3708936; “Verizon

First Global Service Provider to Deploy 100G on U.S. Long-Haul
Network,” news release, Verizon,

http://newscenter.verizon.com/press-releases/verizon/2011/verizon-
first-global-service.xhtml.

8, Facebook, “Key Facts,”

http ://newsroom. fb. com/content/def ault. aspx?Ne ws Areald=2 2.

9, http://www.kurzweilai.net/how-my-predictions-are-faring.

10 , Calculations per Second per $1,000

Year

Calculations per
Second per $1,000

Machine

Natural Logarithm
(caks/sec/$k)

1900

5.82E-06

Analytical Engine

-12.05404

1908

1.30E-04

Hollerith Tabulator

-8.948746

1911

5.79E-05

Monroe Calculator

-9.757311

1919

1.06E-03

IBM Tabulator

-6.84572

1928

6.99E-04

National Ellis 3000

-7.265431

1939

8.55E-03

Zuse2

-4.762175

1940

1.43E-02

Bell Calculator Model 1

-4.246797

1941

4.63E-02

Zuse 3

-3.072613

1943

5.31E+00

Colossus

1.6692151

1946

7.98E-01

ENIAC

-0.225521

1948

3.70E-01

IBMSSEC

-0.994793

1949

1.84E+00

BINAC

0.6081338

1949

1.04E+00

EDSAC

0.0430595

1951

1.43E+00

Univac I

0.3576744

1953

6.10E+00

Univac 1103

1.8089443

1953

1.19E+01

IBM 701

2.4748563

1954

3.67E-01

EDVAC

-1.002666

1955

1.65E+01

Whirlwind

2.8003255

1955

3.44E+00

IBM 704

1.2348899

1958

3.26E-01

Datamatic 1000

-1.121779

1958

1960

1960

1961

1962

1964

1965

1965

1966

1968

1973

1973

1975

1976

1977

1977

1979

1980

1982

1982

1983

1984

9.14E-01

Univac II

-0.089487

1.51E+00

IBM 1620

0.4147552

1.52E+02

DEC PDP-1

5.0205856

2.83E+02

DEC PDP-4

5.6436786

2.94E+01

Univac III

3.3820146

1.59E+02

CDC6600

5.0663853

4.83E+02

IBM 1130

6.1791882

1.79E+03

DEC PDP-8

7.4910876

4.97E+01

IBM 360 Model 75

3.9064073

2.14E+02

DEC PDP-10

5.3641051

7.29E+02

Intellec-8

6.5911249

3.40E+03

Data General Nova

8.1318248

1.06E+04

Altair 8800

9.2667207

7.77E+02

DEC PDP-11 Model 70

6.6554404

3.72E+03

Cray 1

8.2214789

2.69E+04

Apple II

10.198766

1.11E+03

DEC VAX 11 Model 780

7.0157124

5.62E+03

Sun-1

8.6342649

1.27E+05

IBM PC

11.748788

1.27E+05

Compaq Portable

11.748788

8.63E+04

IBM AT-80286

11.365353

8.50E+04

Apple Macintosh

11.350759

1986

5.38E+05

Compaq Deskpro 386

13.195986

1987

2.33E+05

Apple Mac 11

12.357076

1993

3.55E+06

Pentium PC

15.082176

1996

4.81 E+07

Pentium PC

17.688377

1998

1.33E+08

Pentium 11 PC

18.708113

1999

7.03E+08

Pentium III PC

20.370867

2000

1.09E+08

IBM ASCI White

18.506858

2000

3.40E+08

Power Macintosh G4/500

19.644456

2003

2.07E+09

Power Macintosh G5 2.0

21.450814

2004

3.49E+09

Dell Dimension 8400

21.973168

2005

6.36E+09

Power Mac G5 Quad

22.573294

2008

3.50E+10

Dell XPS 630

24.278614

2008

2.07E+10

Mac Pro

23.7534

2009

1.63E+10

Intel Core i7 Desktop

23.514431

2010

5.32E+10

Intel Core i7 Desktop

24.697324

11. Top 500 Supercomputer Sites, http://top500.org/.

12. “Microprocessor Quick Reference Guide,” Intel Research,
http://www.intel.com/pressroom/kits/quickreffam.htm.

IT 1971-2000: VLSI Research Inc.

2001-2006: The International Technology Roadmap for

Semiconductors, 2002 Update and 2004 Update, Table 7a, “Cost—
Near-term Years,” “DRAM cost/bit at (packaged microcents) at
production.”

2007-2008: The International Technology Roadmap for

Semiconductors, 2007, Tables 7a and 7b, “Cost—Near-term Years,”
“Cost—Long-term Years,”

http://www.itrs.net/Links/2007ITRS/ExecSum2007.pdf.

2009-2022: The International Technology Roadmap for

Semiconductors, 2009, Tables 7a and 7b, “Cost—Near-term Years,”
“Cost—Long-term Years,”

http://www.itrs.net/Links/2009ITRS/Home2009.htm.

14, To make all dollar values comparable, computer prices for all years
were converted to their year 2000 dollar equivalent using the
Federal Reserve Board’s CPI data at
http://minneapolisfed.org/research/data/us/calc/. For example, $1

million in 1960 is equivalent to $5.8 million in 2000, and $1
million in 2004 is equivalent to $0.91 million in 2000.

1949:

http://www.cl.cam.ac.uk/UoCCL/misc/EDSAC99/statistics.xhtml,

http://www.davros.org/misc/chronology.xhtml.

1951: Richard E. Matick, Computer Storage Systems and
Technology (New York: John Wiley & Sons, 1977);

http://inventors.about.com/library/weekly/aa062398.htm.

1955: Matick, Computer Storage Systems and Technology;

OECD, 1968, http://members.iinet.net.au/~dgreen/timeline.xhtml.

1960: ftp://rtfm.mit.edu/pub/usenet/alt.sys.pdp8/PDP-

8_Frequently_Asked_Questions_%28posted_every_other_month%29;
http://www.dbit.eom/~greeng3/pdpl/pdpl.xhtml#INTRODUCTION.

1962: ftp://rtfm.mit.edu/pub/usenet/alt.sys.pdp8/PDP-

8_Frequently_Asked_Questions_%28posted_every_other_month%29.

1964: Matick, Computer Storage Systems and Technology;

http://www.research.microsoft.com/users/gbell/craytalk;
http ://www. ddj. com/documents/s=1493/ddj 0005hc/.

1965: Matick, Computer Storage Systems and Technology;

http ://www. f ourmilab. ch/documents/univac/conf ig 1108 .xhtml;
http ://www. f robenius. com/univac .htm.

1968: Data General.

1969, 1970:

http://www.eetimes.com/special/special_issues/millennium/mile
stones/whittier.xhtml.

1974: Scientific Electronic Biological Computer Consulting
(SCELBI).

1975-1996: Byte magazine advertisements.

1997-2000: PC Computing magazine advertisements.

2001: www.pricewatch.com(http://www.jc-news.com/parse.cgi?
news/pricewatch/raw/pw-010702).

2002: www.pricewatch.com(http://www.jc-news.com/parse.cgi?
news/price watch/raw/pw-020624).

2003:

http ://sharkyextreme. com/guide s/WMPG/article .php/10706_222719 1_\
2004: http://www.pricewatch.com (11/17/04).

2008: http://www.pricewatch.com (10/02/08) ($16.61).

15. Dataquest/Intel and Pathfinder Research:

Year $ Log ($)

1968 1.00000000

0

1969 0.85000000

-0.16252

1970 0.60000000

-0.51083

1971 0.30000000

-1.20397

1972 0.15000000

-1.89712

1973 0.10000000

-2.30259

1974 0.07000000

-2.65926

1975 0.02800000

-3.57555

1976 0.01500000

-4.19971

1977 0.00800000

-4.82831

1978 0.00500000

-5.29832

1979 0.00200000

-6.21461

1980 0.00130000

-6.64539

1981 0.00082000

-7.10621

1982 0.00040000

-7.82405

1983 0.00032000

-8.04719

1984 0.00032000

-8.04719

1985 0.00015000

-8.80488

1986 0.00009000

-9.31570

1987 0.00008100

-9.42106

1988 0.00006000

-9.72117

1989 0.00003500

-10.2602

1990 0.00002000

-10.8198

1991 0.00001700

-10.9823

1992 0.00001000

-11.5129

1993 0.00000900

-11.6183

1994 0.00000800

-11.7361

1995 0.00000700

-11.8696

1996 0.00000500

-12.2061

1997 0.00000300

-12.7169

1998 0.00000140

-13.4790

1999 0.00000095

-13.8668

2000 0.00000080

-14.0387

2001 0.00000035 -14.8653

2002 0.00000026 -15.1626

2003 0.00000017 -15.5875

2004 0.00000012 -15.9358

2005 0.000000081 -16.3288

2006 0.000000063 -16.5801

2007 0.000000024 -17.5452

2008 0.000000016 -17.9507

16. Steve Cullen, In-Stat, September 2008, www.instat.com.
Year Mbits Bits

1971921.6 9.216E+08

1972 3788.8 3.789E+09

1973 8294.4 8.294E+09

197419865.6 1.987E+10

1975 42700.8 4.270E+10

1976130662.4 1.307E+11

1977 276070.4 2.761E+11

1978 663859.2 6.639E+11

19791438720.0 1.439E+12

1980 3172761.6 3.173E+12

19814512665.6 4.513E+12

198211520409.6 1.152E+13

1983 29648486.4 2.965E+13

1984 68418764.8 6.842E+13

1985 87518412.8 8.752E+13

1986 192407142.4 1.924E+14

1987 255608422.4 2.556E+14

1988 429404979.2 4.294E+14

1989 631957094.4 6.320E+14

1990 950593126.4 9.506E+14

1991 1546590618 1.547E+15

1992 2845638656 2.846E+15

1993 4177959322 4.178E+15

1994 7510805709 7.511E+15

1995 13010599936 1.301E+16

1996 23359078007 2.336E+16

1997 45653879161 4.565E+16

1998 85176878105 8.518E+16

1999 1.47327E+11 1.473E+17

2000 2.63636E+11 2.636E+17

2001 4.19672E+11 4.197E+17

2002 5.90009E+11 5.900E+17

2003 8.23015E+11 8.230E+17

2004 1.32133E+12 1.321E+18

2005 1.9946E+12 1.995E+18

2006 2.94507E+12 2.945E+18

2007 5.62814E+12 5.628E+18

17. “Historical Notes about the Cost of Hard Drive Storage Space,”

http://www.littletechshoppe.com/nsl625/winchest.xhtml; Byte
magazine advertisements, 1977-1998; PC Computing magazine
advertisements, 3/1999; Understanding Computers: Memory and
Storage (New York: Time Life, 1990);

http://www.cedmagic.com/history/ibm-305-ramac.xhtml; John C.
McCallum, “Disk Drive Prices (1955-2012),”
http://www.jcmit.com/diskprice.htm; IBM, “Frequently Asked
Questions,” http://www-

03.ibm.com/ibm/history/documents/pdf/faq.pdf; IBM, “IBM 355
Disk Storage Unit,” http://www-

03.ibm.com/ibm/history/exhibits/storage/storage_355.xhtml;

IBM, “IBM 3380 Direct Access Storage Device,” http://www.03-
ibm.com/ibm/history/exhibits/storage/storage_3380.xhtml.

18. “Without Driver or Map, Vans Go from Italy to China,” Sydney

Morning Herald, October 29, 2010,

http://www.smh.com.au/technology/technology-news/without-
driver-or-map-vans-go-from-italy-to-china-20101029-
176ja.xhtml.

19. KurzweilAI.net.

20. Adapted with permission from Amiram Grinvald and Rina

Hildesheim, “VSDI: A New Era in Functional Imaging of

Cortical Dynamics,” Nature Reviews Neuroscience 5 (November

2004): 874-85.

The main tools for imaging the brain are shown in this diagram.
Their capabilities are depicted by the shaded rectangles.

Spatial resolution refers to the smallest dimension that can be
measured with a technique. Temporal resolution is imaging time or
duration. There are tradeoffs with each technique. For example, EEG
(electroencephalography), which measures “brain waves” (electrical
signals from neurons), can measure very rapid brain waves (occurring
in short time intervals), but can only sense signals near the surface of
the brain.

In contrast, fMRI (functional magnetic resonance imaging),
which uses a special MRI machine to measure blood flow to neurons
(indicating neuron activity), can sense a lot deeper in the brain (and
spinal cord) and with higher resolution, down to tens of microns
(millionths of a meter). However, fMRI operates very slowly
compared with EEG.

These are noninvasive techniques (no surgery or drugs are
required). MEG (magnetoencephalography) is another noninvasive
technique. It detects magnetic fields generated by neurons. MEG and
EEG can resolve events with a temporal resolution of down to 1
millisecond, but better than fMRI, which can at best resolve events
with a resolution of several hundred milliseconds. MEG also
accurately pinpoints sources in primary auditory, somatosensory, and
motor areas.

Optical imaging covers almost the entire range of spatial and
temporal resolutions, but is invasive. VSDI (voltage-sensitive dyes) is
the most sensitive method of measuring brain activity, but is limited to
measurements near the surface of the cortex of animals.

The exposed cortex is covered with a transparent sealed chamber;
after the cortex is stained with a suitable voltage-sensitive dye, it is
illuminated with light and a sequence of images is taken with a high¬
speed camera. Other optical techniques used in the lab include ion
imaging (typically calcium or sodium ions) and fluorescence imaging
systems (confocal imaging and multiphoton imaging).

Other lab techniques include PET (positron emission tomography,

a nuclear medicine imaging technique that produces a 3-D image),
2DG (2-deoxyglucose postmortem histology, or tissue analysis),
lesions (involves damaging neurons in an animal and observing the
effects), patch clamping (to measure ion currents across biological
membranes), and electron microscopy (using an electron beam to
examine tissues or cells at a very fine scale). These techniques can also
be integrated with optical imaging.

21. MRI spatial resolution in microns (pm), 1980-2012:

Year

Resolution in Citation

microtis

URL

2012

125 "Characterization of

Cerebral White Matter
Properties Using
Quantitative Magnetic
Resonance Imaging Stains'

http://dx.doi.org/10.1089
/brain.2011.0071

2010

200 “Study of Brain Anatomy

with High-Field MRI:

Recent Progress”

http://dx.doi

org/10.1010/)

mri.2010.02.007

2010

250 “High-Resolution Phased-

Array MRI of the Human
Brain at 7 Tesla: Initial
Experience in Multiple
Sclerosis Patients"

http://dx,dot.org/10.1111
/), 1552-6569.2008.00338.X

1994

1,000 “Mapping Human Brain

Activity in Vivo”

http://www.ncbi.nlm
nih.gov/pmc/articles
/PMCI011409/

1989

1.700 “Neurolmaging in Patients

with Seizures of Probable
Frontal Lobe Origin”

http://dx.doi

org/10.111 l/j.1528-1157

1989,tb05470.x

1985

1,700 “A Study of the Septum

Pellucidum and Corpus
Callosum in Schizophrenia
with MR Imaging”

http://dx.doi

.org/10.1111/J. 1600-0447

,1985.tb02634.X

1983

1.700 “Clinical Efficiency of

Nuclear Magnetic Reso¬
nance Imaging"

http://radiology.rsna.org

/content/l46/l/l23.short

1980

5,000 “In Vivo NMR Imaging in

Medicine: The Aberdeen
Approach, Both Physical
and Biological [and
Discussion]”

http://dx doi.orgT0.l098
/rstb 1980.0071

22. Spatial resolution in nanometers (nm) of destructive imaging
techniques, 1983-2011:

Year

2011

2011

2011

200*4

2004

199A

1994

19W

x-y re s
(nm)

4

4

4

13

20

100

2000

3000

Citation

URL

Technique

Softs

'Focused Ion Beam

Millingand Scanning
Electron Microscopy of
Brain Tissue"

hujv/MxJm

org/IO.3791/2388

Focused ion beam/
scanning electron
microscope (T1B/SEM)

"Volume Electron
Microscopy for Neuronal
Circuit Reconstruction"

http://dx.doi.org/l0.10l6
/).cori>.20ll 10,022

Scanning electron
m k roscopy (S E M)

’’Volume Electron
Microscopy for Neuronal

Ci rc u it Reconst rue lion"

http://dx.doi.org/10.1016
/).conb. 2011.10.022

Transmission electron
microscopy (TEM)

"Scnal Block- Face Scan¬
ning Electron Microscopy
to Reconstruct Threc-
I hmcnsKUM 1T issue

Nanostructure”

hi tp: ltd vdoi.org/10.1371
/tour na 1 pbkv0020329

Serial hlock-face scanning
electron microscopy
(SBF-SEM)

Result quoted in http.//
faculty vs. t a mu edu/choe
/It p'pu hlicat ions/cho c
hpcOR-prepnntpdf.
provided by Yoonsuck
(!hoe.

“Wet SUM: A Novd

Method for Rapid

Diagnosis of Brain

Tiinton"

http ://dx dot
org/10.1080

A)19I3I2049

0515603

"Wet" scanning elect ton
mi cToscopy (wet SE M)

“A Depolarizing Chloride
Current Contributes to
Chcmoeftect rival
Transduction in

Olfactory Sensory

Neurons in Situ*

ht tp: tt w w w.jncu rose i
.org/contcnt/18/17

A»623.full

Scanning transmtision
electron microscope
(STEM)

'Enhanced Optical Imag¬
ing of Rat Ghoinas and
Tumor Margins"

ht tp. //journ al v hvw
.com/neu rosu rgcry
/Abstract/1994/11000
/ Enh an ced.Optical
_1 roag>ng^of_Rat_( ilK*mas
_and_Tumor. I9.a%px

Enhanced optical imaging

With a spatial resolution
of the optical images
below 20 microns 2'pixel
(22).

"3D Imaging of X-Ray
Microscopy"

http://wwwjkCfwess.org

/c-hhrary/»f2/pdf/OI05

PDF

Projection microscopy

Sec Fig 7 in article.

23. Spatial resolution in microns (pm) of nondestructive imaging
techniques in animals, 1985-2012:

Year Finding

2012 Resolution 0.07

Citation

URL

Technique

Sebastian Berning et al., “Nanoscopy in a Living Mouse
Brain,” Science 335, no. 6068 (February 3, 2012): 551.
http://dx.doi.org/10.1126/science.1215369
Stimulated emission depletion (STED) fluorescence
nanoscopy

Notes

2012 Resolution
Citation
URL

Technique
2004 Resolution

Citation

URL

Technique

Notes

1996 Resolution

Citation

URL

Technique

Notes

1995 Resolution

Citation

URL

Technique

Highest resolution achieved in vivo so far
0.25

Sebastian Berning et al., “Nanoscopy in a Living Mouse
Brain,” Science 335, no. 6068 (February 3, 2012): 551.
http://dx.doi.org/10.1126/science.1215369
Confocal and multiphoton microscopy
50

Amiram Grinvald and Rina Hildesheim, “VSDI: A New
Era in Functional Imaging of Cortical Dynamics,” Nature
Reviews Neuroscience 5 (November 2004): 874-85.
http://dx.doi.org/10.1038/nrnl536
Imaging based on voltage-sensitive dyes (VSDI)

“VSDI has provided high-resolution maps, which
correspond to cortical columns in which spiking occurs,
and offer a spatial resolution better than 50 pm.”

50

Dov Malonek and Amiram Grinvald, “Interactions
between Electrical Activity and Cortical Microcirculation
Revealed by Imaging Spectroscopy: Implications for
Functional Brain Mapping,” Science 272, no. 5261 (April
26, 1996): 551-54.

http://dx.doi.org/10.1126/science.272.5261.551
Imaging spectroscopy

“The study of spatial relationships between individual
cortical columns within a given brain area has become
feasible with optical imaging based on intrinsic signals, at
a spatial resolution of about 50 pm.”

50

D. H. Turnbull et al., “Ultrasound Backscatter Microscope
Analysis of Early Mouse Embryonic Brain
Development,” Proceedings of the National Academy of
Sciences 92, no. 6 (March 14, 1995): 2239-43.
http ://www.pnas. org/content/92/6/2239. short
Ultrasound backscatter microscopy
“We demonstrate application of a real-time imaging

method called ultrasound backscatter microscopy for
Notes visualizing mouse early embryonic neural tubes and
hearts. This method was used to study live embryos in
utero between 9.5 and 11.5 days of embryogenesis, with a
spatial resolution close to 50 pm.”

1985 Resolution 500

H. S. Orbach, L. B. Cohen, and A. Grinvald, “Optical
Citation Mapping of Electrical Activity in Rat Somatosensory and
Visual Cortex,” Journal of Neuroscience 5, no. 7 (July 1,
1985): 1886-95.

URL http ://www. j neurosci. org/content/5/7/1886. short

Technique Optical methods

Chapter 11: Objections

L Paul G. Allen and Mark Greaves, “Paul Allen: The Singularity Isn’t
Near,” Technology Review, October 12, 2011,

http://www.technologyreview.com/blog/guest/27206/.

Z ITRS, “International Technology Roadmap for Semiconductors,”
http://www.itrs.net/Links/2011ITRS/Home2011.htm.

T Ray Kurzweil, The Singularity Is Near (New York: Viking, 2005),
chapter 2.

4 Endnote 2 in Allen and Greaves, “The Singularity Isn’t Near,” reads
as follows: “We are beginning to get within range of the computer
power we might need to support this kind of massive brain
simulation. Petaflop-class computers (such as IBM’s BlueGene/P
that was used in the Watson system) are now available
commercially. Exaflop-class computers are currently on the drawing
boards. These systems could probably deploy the raw computational
capability needed to simulate the firing patterns for all of a brain’s
neurons, though currently it happens many times more slowly than
would happen in an actual brain.”

5, Kurzweil, The Singularity Is Near, chapter 9, section titled “The
Criticism from Software” (pp. 435-42).

£L Ibid., chapter 9.

Z Although it is not possible to precisely determine the information
content in the genome, because of the repeated base pairs it is
clearly much less than the total uncompressed data. Here are two
approaches to estimating the compressed information content of the
genome, both of which demonstrate that a range of 30 to 100
million bytes is conservatively high.

1. In terms of the uncompressed data, there are 3 billion DNA
rungs in the human genetic code, each coding 2 bits (since there are
four possibilities for each DNA base pair). Thus the human genome is
about 800 million bytes uncompressed. The noncoding DNA used to
be called “junk DNA,” but it is now clear that it plays an important
role in gene expression. However, it is very inefficiently coded. For
one thing, there are massive redundancies (for example, the sequence
called “ALU” is repeated hundreds of thousands of times), which

compression algorithms can take advantage of.

With the recent explosion of genetic data banks, there is a great
deal of interest in compressing genetic data. Recent work on applying
standard data compression algorithms to genetic data indicates that
reducing the data by 90 percent (for bit perfect compression) is
feasible: Hisahiko Sato et al., “DNA Data Compression in the Post
Genome Era,” Genome Informatics 12 (2001): 512-14,

http://www.jsbi.org/journal/GIW01/GIW01P130.pdf.

Thus we can compress the genome to about 80 million bytes
without loss of information (meaning we can perfectly reconstruct the
full 800-million-byte uncompressed genome).

Now consider that more than 98 percent of the genome does not
code for proteins. Even after standard data compression (which
eliminates redundancies and uses a dictionary lookup for common
sequences), the algorithmic content of the noncoding regions appears
to be rather low, meaning that it is likely that we could code an
algorithm that would perform the same function with fewer bits.
However, since we are still early in the process of reverse-engineering
the genome, we cannot make a reliable estimate of this further
decrease based on a functionally equivalent algorithm. I am using,
therefore, a range of 30 to 100 million bytes of compressed
information in the genome. The top part of this range assumes only
data compression and no algorithmic simplification.

Only a portion (although the majority) of this information
characterizes the design of the brain.

2. Another line of reasoning is as follows. Though the human
genome contains around 3 billion bases, only a small percentage, as
mentioned above, codes for proteins. By current estimates, there are
26,000 genes that code for proteins. If we assume those genes average
3,000 bases of useful data, those equal only approximately 78 million
bases. A base of DNA requires only 2 bits, which translate to about 20
million bytes (78 million bases divided by four). In the protein-coding
sequence of a gene, each “word” (codon) of three DNA bases
translates into one amino acid. There are, therefore, 4 3 (64) possible
codon codes, each consisting of three DNA bases. There are, however,
only 20 amino acids used plus a stop codon (null amino acid) out of
the 64. The rest of the 43 codes are used as synonyms of the 21 useful
ones. Whereas 6 bits are required to code for 64 possible

combinations, only about 4.4 (log 2 21) bits are required to code for 21
possibilities, a savings of 1.6 out of 6 bits (about 27 percent), bringing
us down to about 15 million bytes. In addition, some standard
compression based on repeating sequences is feasible here, although
much less compression is possible on this protein-coding portion of the
DNA than in the so-called junk DNA, which has massive
redundancies. So this will bring the figure probably below 12 million
bytes. However, now we have to add information for the noncoding
portion of the DNA that controls gene expression. Although this
portion of the DNA constitutes the bulk of the genome, it appears to
have a low level of information content and is replete with massive
redundancies. Estimating that it matches the approximately 12 million
bytes of protein-coding DNA, we again come to approximately 24
million bytes. From this perspective, an estimate of 30 to 100 million
bytes is conservatively high.

8, Dharmendra S. Modha et al., “Cognitive Computing,”
Communications of the ACM 54, no. 8 (2011): 62-71,

http://cacm.acm.org/magazines/2011/8/114944-cognitive-
computing/fulltext.

9* Kurzweil, The Singularity Is Near, chapter 9, section titled “The
Criticism from Ontology: Can a Computer Be Conscious?” (pp.
458-69).

10, Michael Denton, “Organism and Machine: The Flawed Analogy,”

in Are We Spiritual Machines? Ray Kurzweil vs. the Critics of
Strong AT (Seattle: Discovery Institute, 2002).

11. Hans Moravec, Mind Children (Cambridge, MA: Harvard
University Press, 1988).

Epilogue

L “In U.S., Optimism about Future for Youth Reaches All-Time Low,”
Gallup Politics, May 2, 2011,

http://www.gallup.com/poll/147350/optimism-future-youth-reaches-
time-low.aspx.

T James C. Riley, Rising Life Expectancy: A Global History
(Cambridge: Cambridge University Press, 2001).

T J. Bradford DeLong, “Estimating World GDP, One Million B.C.—
Present,” May 24, 1998,

http://econl61.berkeley.edu/TCEH/1998_Draft/World_GDP/Estimat
and http://futurist.typepad.com/my_weblog/2007/07/economic-
growth.xhtml. See also Peter H. Diamandis and Steven Kotler,
Abundance: The Future Is Better Than You Think (New York: Free
Press, 2012).

4 Martine Rothblatt, Transgender to Transhuman (privately printed,
2011). She explains how a similarly rapid trajectory of acceptance is
most likely to occur for “transhumans,” for example, nonbiological
but convincingly conscious minds as discussed in chapter 9.

5, The following excerpt from The Singularity Is Near, chapter 3 (pp.
133-35), by Ray Kurzweil (New York: Viking, 2005), discusses the
limits of computation based on the laws of physics:

The ultimate limits of computers are profoundly high. Building
on work by University of California at Berkeley Professor Hans
Bremermann and nanotechnology theorist Robert Freitas, MIT
Professor Seth Lloyd has estimated the maximum computational
capacity, according to the known laws of physics, of a computer
weighing one kilogram and occupying one liter of volume—about the
size and weight of a small laptop computer—what he calls the
“ultimate laptop.”

[Note: Seth Lloyd, “Ultimate Physical Limits to Computation,”
Nature 406 (2000): 1047-54.

[Early work on the limits of computation were done by Hans J.
Bremermann in 1962: Hans J. Bremermann, “Optimization Through
Evolution and Recombination,” in M. C. Yovits, C. T. Jacobi, C. D.

Goldstein, eds., Self-Organizing Systems (Washington, D.C.: Spartan
Books, 1962), pp. 93-106.

[In 1984 Robert A. Freitas Jr. built on Bremermann’s work in
Robert A. Freitas Jr., “Xenopsychology,” Analog 104 (April 1984):
41-53,

http://www.rfreitas.eom/Astro/Xenopsychology.htm#SentienceQuotien

The potential amount of computation rises with the available
energy. We can understand the link between energy and computational
capacity as follows. The energy in a quantity of matter is the energy
associated with each atom (and subatomic particle). So the more
atoms, the more energy. As discussed above, each atom can potentially
be used for computation. So the more atoms, the more computation.
The energy of each atom or particle grows with the frequency of its
movement: the more movement, the more energy. The same
relationship exists for potential computation: the higher the frequency
of movement, the more computation each component (which can be an
atom) can perform. (We see this in contemporary chips: the higher the
frequency of the chip, the greater its computational speed.)

So there is a direct proportional relationship between the energy
of an object and its potential to perform computation. The potential
energy in a kilogram of matter is very large, as we know from
Einstein’s equation E = me 2 . The speed of light squared is a very large
number: approximately 10 17 meter 2 /second 2 . The potential of matter to
compute is also governed by a very small number, Planck’s constant:
6.6 x 10“ 34 joule-seconds (a joule is a measure of energy). This is the
smallest scale at which we can apply energy for computation. We
obtain the theoretical limit of an object to perform computation by
dividing the total energy (the average energy of each atom or particle
times the number of such particles) by Planck’s constant.

Lloyd shows how the potential computing capacity of a kilogram
of matter equals pi times energy divided by Planck’s constant. Since
the energy is such a large number and Planck’s constant is so small,
this equation generates an extremely large number: about 5 x lO 50
operations per second.

[Note: tt x maximum energy (10 17 kg x meter 2 /second 2 ) / (6.6 x
1(T 34 ) joule-seconds) = ~ 5 x 10 50 operations/second.]

If we relate that figure to the most conservative estimate of
human brain capacity (10 19 cps and lO 10 humans), it represents the

equivalent of about 5 billion trillion human civilizations.

[Note: 5 x 10 50 cps is equivalent to 5 x 10 21 (5 billion trillion)
human civilizations (each requiring 10 29 cps).]

If we use the figure of 10 16 cps that I believe will be sufficient for
functional emulation of human intelligence, the ultimate laptop would
function at the equivalent brain power of 5 trillion trillion human
civilizations.

[Note: Ten billion (10 10 ) humans at 10 16 cps each is 10 26 cps for
human civilization. So 5 x 10 5 ° cps is equivalent to 5 x 10 24 (5 trillion
trillion) human civilizations.]

Such a laptop could perform the equivalent of all human thought
over the last ten thousand years (that is, ten billion human brains
operating for ten thousand years) in one ten-thousandth of a
nanosecond.

[Note: This estimate makes the conservative assumption that
we’ve had ten billion humans for the past ten thousand years, which is
obviously not the case. The actual number of humans has been
increasing gradually over the past to reach about 6.1 billion in 2000.
There are 3 x 10 7 seconds in a year, and 3 x 10 11 seconds in ten
thousand years. So, using the estimate of 10 26 cps for human
civilization, human thought over ten thousand years is equivalent to
certainly no more than 3 x 10 37 calculations. The ultimate laptop
performs 5 x 10 5 ° calculations in one second. So simulating ten
thousand years of ten billion humans’ thoughts would take it about 10”
13 seconds, which is one ten-thousandth of a nanosecond.]

Again, a few caveats are in order. Converting all of the mass of
our 2.2-pound laptop into energy is essentially what happens in a
thermonuclear explosion. Of course, we don’t want the laptop to
explode but to stay within its one-liter dimension. So this will require
some careful packaging, to say the least. By analyzing the maximum
entropy (degrees of freedom represented by the state of all the
particles) in such a device, Lloyd shows that such a computer would
have a theoretical memory capacity of 10 31 bits. It’s difficult to
imagine technologies that would go all the way in achieving these
limits. But we can readily envision technologies that come reasonably
close to doing so. As the University of Oklahoma project shows, we
already demonstrated the ability to store at least fifty bits of
information per atom (although only on a small number of atoms, so

far). Storing 10 27 bits of memory in the 10 25 atoms in a kilogram of
matter should therefore be eventually achievable.

But because many properties of each atom could be exploited to
store information—such as the precise position, spin, and quantum
state of all of its particles—we can probably do somewhat better than
10 27 bits. Neuroscientist Anders Sandberg estimates the potential
storage capacity of a hydrogen atom at about four million bits. These
densities have not yet been demonstrated, however, so weTl use the
more conservative estimate.

[Note: Anders Sandberg, “The Physics of the Information
Processing Superobjects: Daily Life Among the Jupiter Brains,”
Journal of Evolution and Technology 5 (December 22, 1999),
http://www.transhumanist.com/volume5/Brains2.pdf.]

As discussed above, 10 42 calculations per second could be
achieved without producing significant heat. By fully deploying
reversible computing techniques, using designs that generate low
levels of errors, and allowing for reasonable amounts of energy
dissipation, we should end up somewhere between 10 42 and 10 50
calculations per second.

The design terrain between these two limits is complex.
Examining the technical issues that arise as we advance from 10 42 to
10 50 is beyond the scope of this chapter. We should keep in mind,
however, that the way this will play out is not by starting with the
ultimate limit of 10 50 and working backward based on various
practical considerations. Rather, technology will continue to ramp up,
always using its latest prowess to progress to the next level. So once
we get to a civilization with 10 42 cps (for every 2.2 pounds), the
scientists and engineers of that day will use their essentially vast
nonbiological intelligence to figure out how to get 10 43 , then 10 44 , and
so on. My expectation is that we will get very close to the ultimate
limits.

Even at 10 42 cps, a 2.2-pound “ultimate portable computer”
would be able to perform the equivalent of all human thought over the
last ten thousand years (assumed at ten billion human brains for ten
thousand years) in ten microseconds.

[Note: See note above. 10 42 cps is a factor of 1CT 8 less than 10 5 °
cps, so one ten-thousandth of a nanosecond becomes 10
microseconds.]

If we examine the Exponential Growth of Computing chart
( chapter 2 ). we see that this amount of computing is estimated to be
available for one thousand dollars by 2080.

INDEX

Page numbers in italics refer to graphs and illustrations,
abortion, 212-13

Abundance (Diamandis and Kotler), 278
Ackerman, Diane, 179
ACTH (adrenocorticotropin), 107
addictive behaviors, 105-6, 118
adrenal glands, 107
adrenaline, 107

Age of Intelligent Machines, The (Kurzweil), 4, 165-66, 256-57

Age of Spiritual Machines, The (Kurzweil), 4, 257, 267

A.I. (film), 210

Aiken, Howard, 189

Alexander, Richard D., 224

algorithms, intelligent, 6-7

Allen, Paul, 266-72

Allman, John M., 179

Alzheimer’s disease, 102

amygdala, 71, 77, 106-8, 109

analog processing, digital emulation of, 194-95, 274

Analytical Engine, 189-90

anesthesia awareness, 206

animal behavior, evolution of, 122

apical dendrites, 110

aptitude, 111-12

artificial intelligence (AI), 7, 37-38, 50, 265, 280

Allen on, 270-71

biological models for, 273

chess playing and, 6, 38-39, 165-66, 257

conversation and, 168-69

as extension of neocortex, 172, 276
knowledge bases and, 4, 6-7, 170-71, 246, 247

language and speech processing in, 72-73, 92, 115-16, 122-23, 128, 135-
41, 142-46, 145, 149-50, 152-53, 156, 157-72
medicine and, 6-7, 39, 108, 156, 160-61, 168
omnipresence of, 158
optimization of pattern recognition in, 112
sparse coding in, 95-96
see also neocortex, digital
Audience, Inc., 96-97, 98
auditory association, 77
auditory cortex, 7, 77, 97, 128
auditory information processing, 96-97, 97
auditory nerve, 97, 128
data reduction in, 138
auditory pathway, 97
autoassociation, 59-61, 133, 173
automobiles, self-driving, 7, 159, 261, 274
axons, 36, 42, 43, 66, 67, 90, 100, 113, 150, 173
as digital processors, 191

Babbage, Charles, 189-90
Bainbridge, David, 179
bandwidth, of Internet, 254
basis functions, 103-4
Bedny, Marina, 87
Bell System Technical Journal, 184
Berger, Theodore, 102
Berners-Lee, Tim, 172
Bernoulli’s principle, 5, 8

Better Angels of Our Nature: Why Violence Has Declined (Pinker), 27

Bierce, Ambrose, 66

BINAC, 189

Bing, 171

biology, 37

DNA as unifying theory of, 17
reverse-engineering of, 4-5
biomedicine, LOAR and, 251, 252, 253
Blackmore, Susan, 211

Blade Runner (film), 210
Blakeslee, Sandra, 73, 156
Blue Brain Project, 63, 80, 124-28 ,125
Bombe, 187

Bostrom, Nick, 129-30, 222

Boyden, Ed, 126

brain, evolution of, 2

brain, human:

analog computing in, 274

complexity of, 8-9, 181, 272

digital implants in, 243-44

digital neocortex as extension of, 172, 276

hemispheres of, 77, 224-49

LOAR as applied to, 261-63, 263, 264, 265

prediction by, 250

redundancy of, 9

reverse-engineering of, see brain, human, computer emulation of;

neocortex, digital
structure of, 77

brain, human, computer emulation of, 5, 7, 179-98, 273, 280

invariance and, 197

memory requirements of, 196-97

parallel processing in, 197

processing speed in, 195-96

redundancy in, 197

singularity and, 194

Turing test and, 159-60, 169, 170, 178, 191, 213, 214, 233, 276, 298n
von Neumann on, 191-95
see also neocortex, digital
brain, mammalian:

hierarchical thinking as unique to, 2-3, 35
neocortex in, 78, 93, 286n

brain plasticity, 79, 87-89, 91, 182, 193, 197, 225, 280

as evidence of universal neocortical processing, 86, 88, 152

limitations on, 88-89

brain scanning, 7, 263, 308 n

destructive, 264, 265, 309n-lln

LOAR and, 262-63, 263, 264, 265

nondestructive, 127, 129, 264, 312n-13n

noninvasive, 273

Venn diagram of, 262

brain simulations, 124-31, 262

brain stem, 36, 99

Bremermann, Hans, 316n

Britain, Battle of, 187

Brodsky, Joseph, 199

Burns, Eric A., 113

busy beaver problem, 207

Butler, Samuel, 62, 199-200, 224, 248-49

Byron, Ada, Countess of Lovelace, 190, 191

California, University of, at Berkeley, 88
“CALO” project, 162

carbon atoms, information structures based on, 2

Carroll, Lewis, 109

cells, replacement of, 245, 246

cellular automata, 236-39

cerebellum, 7, 77, 103-4

uniform structure of, 103

cerebral cortex, 7-8

see also neocortex

Chalmers, David, 201-2, 218, 241

“chatbots,” 161

chemistry, 37

chess, AI systems and, 6, 38-39, 165-66, 257

chimpanzees:

language and, 3, 41

tool use by, 41

“Chinese room” thought experiment, 170, 274-75

Chomsky, Noam, 56, 158

Church, Alonzo, 186

Church-Turing thesis, 186

civil rights, 278

cloud computing, 116-17, 123, 246, 279-80

cochlea, 96, 97, 135, 138

cochlear implants, 243

Cockburn, David, 214

Cold Spring Harbor Laboratory, 129

Colossus, 187, 188
“common sense,” 40

communication, reliability of, 182-85, 190

communication technology, LOAR and, 253, 254

compatibilism, 234

complexity, 198, 233

of human brain, 8-9, 181, 272

modeling and, 37-38

true vs. apparent, 10-11

computation:

price/performance of, 4-5, 250-51, 257, 257, 267-68, 301n-3n
thinking compared with to, 26-27
universality of, 26, 181-82, 185, 188, 192, 207
Computer and the Brain, The (von Neumann), 191
computers:

brain emulated by, see brain, human, computer emulation of
consciousness and, 209-11, 213-15, 223
intelligent algorithms employed by, 6-7
knowledge base expanded by, 4, 246, 247
logic gates in, 185

memory in, 185, 259, 260, 268, 301n-3n, 306n-7n
reliability of communication by, 182-85, 190
see also neocortex, digital

“Computing Machinery and Intelligence” (Turing), 191

conditionals, 65, 69, 153, 189, 190

confabulation, 70, 217, 227, 228, 229

connectionism, 133, 191

“connectome,” 262

consciousness, 11, 199-209

cerebral hemispheres and, 226-29

computers and, 209-11, 213-15, 223, 233

Descartes on, 221-22

dualist views of, 202-3

Eastern vs. Western views of, 218-24

free will and, 233-34

Kurzweil’s thought experiment on, 210

leap-of-faith view of, 209-10, 233

as meme, 211, 235

memory and, 28-29, 206-7, 217

moral and legal systems as based on, 212-13

of nonhuman life-forms, 213-14

panprotopsychist view of, 203, 213

as philosophical construct, 201-9

qualia and, 203-5, 211

as scientifically unverifiable, 205, 211, 228

as spiritual construct, 222-23

as subjective experience, 211

Wittgenstein on, 220-21

zombie thought experiment and, 202

conversation, AI and, 168-69

convictions, courage of, 11, 23-24, 112, 117

Cooper’s law, 253

corpus callosum, 70, 77, 226

cortical association areas, 58

cortisol, 107

Craig, Arthur, 100

creativity, 113-17

and expansion of neocortex, 116-17
Cretaceous-Paleogene extinction event, 79
Crick, Francis, 16-17
critical thinking, 6, 176, 197
“criticism from incredulity,” 266-72
Crookes radiometer, 20-21, 21
crossbar switching, 85
Curtiss, Susan, 225
Cybernetics (Wiener), 115
Cyc project, 162, 164

D2 gene, 106

Dalai Lama, 109

Dalrymple, David, 124, 291n

DARPA, 162, 163

Darwin, Charles, 15

Lyell’s influence on, 14-15, 114, 177

thought experiments of, 14-16, 23

data, determined vs. predictable, 26, 239

data traffic, on Internet, 254

de Bode, Stella, 225

Deep Blue, 39, 166
DeMille, Cecil B., 113
dendrites, 42, 43, 66, 67, 90, 150
as analog processors, 191-92
apical, 110

Dennett, Daniel, 205-6, 230, 234

Denton, Michael, 275-76

Descartes, Rene, 221-22, 240

destructive imaging techniques, 264, 265, 309n-lln

determined outcomes, predictable outcomes vs., 26, 239

determinism, 232-33

free will and, 232-33, 234

randomness and, 236

Devil’s Dictionary, The (Bierce), 66

Diamandis, Peter, 278

Diamond, Marian, 25

Dickinson, Emily, 1

diffusion tractography, 129

digital processors:

emulation of analog processing in, 194-95, 274
see also computers; neocortex, digital
Diogenes Laertius, 246
DNA, 9-10

animal behavior encoded in, 122
discovery and description of, 16-17
encoding of information in, 2, 17
as unifying theory of biology, 17
see also genome, human
dopamine, 105-6, 107, 118
Dostoevsky, Fyodor, 199
Dragon Dictation, 152-53
Dragon Go!, 162, 164
Dragon Naturally Speaking, 152
Drave, Scott, 149
dreams:

conscious thinking vs., 71-72

taboos and, 71-72

as undirected thoughts, 70-72

dynamic RAM memory, growth in, 259, 301n-3n

E = me 2, 22
Eckert, J. Presper, 189
EDSAC, 189
EDVAC, 189

Einstein, Albert, 11, 25, 35, 71

thought experiments of, 18-23, 114, 117

Electric Sheep, 149

Emerson, Ralph Waldo, 13

emotional intelligence, 110, 194, 201, 213

emotions:

high-level, 109-11

as products of both old brain and neocortex, 107-8

energy, mass equivalent of, 22-23

ENIAC, 180, 189

Enigma coding machine, 187

EPAM (elementary perceiver and memorizer), 37-38

estrogen, 118

ether theory, Michelson-Morley disproof of, 18, 19, 36, 114

evolution, 76-79

Darwin’s theory of, 14-16

encoding of information and, 2

intelligence as goal of, 76-78, 277, 278

LOAR and, 4

of neocortex, 35-36

of simulated organisms, 147-53

as spiritual process, 223

survival as goal of, 79, 104, 242

see also natural selection

evolutionary (genetic) algorithms (GAs), 147-53, 173
existentialism, 221

expectation (excitatory) signals, 42, 52, 54, 60, 67, 73, 85, 91, 100, 112,
173, 175, 196-97
expert managers, 166-67, 168
experts, core knowledge of, 39-40
exponential growth, see law of accelerating returns
eye movement, pattern recognition and, 73

Far Side, 277

fear, in old and new brains, 104-8

feature invariance, see invariance
feedforward neural net, 134, 135
Feldman, Daniel E., 88
Felleman, Daniel J., 86
fetus, brain of, 62

field programmable gate array (FPGA), 83

“fight or flight” mechanism, 107, 118

“First Draft of a Report on EDVAC” (von Neumann), 188

Forest, Craig, 126

formants, 135 ,137

fractals, 9, 10-11 ,10

Franklin, Rosalind, 16-17 ,17

free will, 11, 224-40

consciousness and, 233-34

definition of, 231-32

determinism and, 232-33, 234

as meme, 235

responsibility and, 235

Freitas, Robert, 316n

Freud, Sigmund, 66, 71, 72

Friston, K. J., 75

frontal lobe, 36, 41, 77

fusiform gyrus, 89, 95, 111

gambling, 106
ganglion cells, 95

Garcia Marquez, Gabriel, 3-4, 283n-85n
Gazzaniga, Michael, 226-29, 234
General Electric, 149

genetic (evolutionary) algorithms, see evolutionary (genetic) algorithms
genome, human, 4, 103, 251

design information encoded in, 90, 147, 155, 271, 314n-15n

redundancy in, 271, 314n, 315n

sequencing of, LOAR and, 252, 252, 253

see also DNA

George, Dileep, 41, 73, 156

Ginet, Carl, 234

God, concept of, 223

Godel, Kurt, 187

incompleteness theorem of, 187, 207-8
“God parameters,” 147
Good, Irvin J., 280-81
Google, 279

self-driving cars of, 7, 159, 261, 274
Google Translate, 163
Google Voice Search, 72, 161
Greaves, Mark, 266-72
Grossman, Terry, 287n-88n
Grotschel, Martin, 269

Hameroff, Stuart, 206, 208, 274
Hameroff-Penrose thesis, 208-9
Hamlet (Shakespeare), 209
Harnad, Stevan, 266

Harry Potter and the Half-Blood Prince (Rowling), 117

Harry Potter and the Prisoner ofAzkaban (Rowling), 121

Hasson, Uri, 86

Havemann, Joel, 25

Hawkins, Jeff, 41, 73, 156

Hebb, Donald 0., 79-80

Hebbian learning, 80

hemispherectomy, 225

hidden Markov models (HMMs), 68, 141-44, 143,145, 147, 162
hierarchical hidden Markov models (HHMMs), 51, 68, 72, 74, 144-46,
149-50, 152-53, 155, 156, 162, 164, 167-68, 195, 269, 270
pmning of unused connections by, 144, 147, 155
hierarchical learning, 164, 195, 197
hierarchical memory:
digital, 156-57
temporal, 73

hierarchical systems, 4, 35

hierarchical thinking, 8, 69, 105, 117, 153-54, 177, 233, 286n

bidirectional flow of information in, 52

language and, 56, 159, 162, 163

in mammalian brain, 2-3

pattern recognition as, 33, 41-53

recursion in, 3, 7-8, 56, 65, 91, 109, 156, 177

routine tasks and, 32-33

as survival mechanism, 79

as unique to mammalian brain, 35

hippocampus, 63, 77, 101-2

Hitchhiker’s Guide to the Galaxy, The (Adams), 161

Hobbes, Thomas, 278

Hock, Dee, 113

Horwitz, B., 75

Hubei, David H., 34

Human Connectome Project, 129

human genome, see genome, human

Human Genome Project, 251, 273

humans:

merger of intelligent technology with, 266-72, 276, 279-82
tool-making ability of, 3, 27, 276, 279
Hume, David, 234-35

IBM, 6-7, 108, 128, 165-66

Cognitive Computing Group of, 195

ideas, recursive linking of, 3

identity, 10, 11, 240-47

as pattern continuity, 246, 247

thought experiments on, 242-47

importance parameters, 42, 60, 66, 67

incompatibilism, 234, 236

incompleteness, GodePs theorem of, 187, 207-8

inference engines, 162-63

information, encoding of:

in DNA, 2, 17, 122

evolution and, 2

in human genome, 90, 147, 155, 271, 314n-15n
information structures, carbon-based, 2
information technologies:
exponential growth of, 278-79

LOAR and, 4, 249-57, 252, 257, 258, 259, 260, 261, 261

inhibitory signals, 42, 52-53, 67, 85, 91, 100, 173

insula, 99-100, 99, 110

integrated circuits, 85

Intel, 268

intelligence, 1-2

emotional, 110, 194, 201, 213
as evolutionary goal, 76-78, 277, 278
evolution of, 177
as problem-solving ability, 277

International Dictionary of Psychology (Sutherland), 211
“International Technology Roadmap for Semiconductors,” 268
Internet, exponential growth of, 254
Interpretation of Dreams, The (Freud), 66
intuition, linear nature of, 266

invariance, in pattern recognition, 30, 59-61, 133, 135, 137, 175

and computer emulation of brain, 197

one-dimensional representations of data and, 141-42

vector quantization and, 141

inventors, timing and, 253, 255

I, Robot (film), 210

Jacquard loom, 189, 190
James, William, 75-76, 98-99
Jeffers, Susan, 104
Jennings, Ken, 157-58, 165

Jeopardy! (TV show), 6-7, 108, 157-58, 160, 165, 166, 167, 168, 169, 172,
178, 232-33, 270
Joyce, James, 55

Kasparov, Garry, 39, 166
K Computer, 196
knowledge bases:

AI systems and, 4, 6-7, 170-71, 246, 247

of digital neocortex, 177

exponential growth of, 3

as inherently hierarchical, 220

language and, 3

professional, 39-40

as recursively linked ideas, 3

Kodandaramaiah, Suhasa, 126

Koene, Randal, 89

Koltsov, Nikolai, 16

Kotler, Steven, 278

KurzweilAI.net, 161

Kurzweil Applied Intelligence, 144
Kurzweil Computer Products, 122
Kurzweil Voice, 160

lamina 1 neurons, 97
language:

chimpanzees and, 3, 41

and growth of knowledge base, 3

hierarchical nature of, 56, 159, 162, 163

as metaphor, 115

as translation of thinking, 56, 68

language software, 51, 72-73, 92, 115-16, 122-23, 144-45, 145, 156, 157-
72, 174, 270

expert managers in, 166-67
hand-coded rules in, 164-65, 166, 168
HHMMs in, 167-68
hierarchical systems in, 162-65
Larson, Gary, 277

“Last Voyage of the Ghost, The” (Garcia Marquez), 3-4

lateral geniculate nucleus, 95, 100

law of accelerating returns (LOAR), 4, 6, 7, 41, 123

as applied to human brain, 261-63, 263, 264, 265

biomedicine and, 251, 252, 253

communication technology and, 253, 254

computation capacity and, 281, 316n-19n

information technology and, 4, 249-57, 252, 257, 258, 259, 260, 261, 261
objections to, 266-82

predictions based on, 256-57, 257, 258, 259, 260, 261
and unlikelihood of other intelligent species, 5
“Law of Accelerating Returns, The” (Kurzweil), 267
laws of thermodynamics, 37, 267
learning, 61-65, 122, 155, 273-74
conditionals in, 65

and difficulty of grasping more than one conceptual level at a time, 65
in digital neocortex, 127-28, 175-76
environment and, 119
Hebbian, 80

hierarchical, 164, 195, 197
in neural nets, 132-33

neurological basis of, 79-80

pattern recognition as basic unit of, 80-81

of patterns, 63-64, 90

recognition as simultaneous with, 63

simultaneous processing in, 63, 146

legal systems, consciousness as basis of, 212-13

Leibniz, Gottfried Wilhelm, 34, 223

Lenat, Douglas, 162

Leviathan (Hobbes), 278

Lewis, Al, 93

Libet, Benjamin, 229-30, 231, 234
light, speed of, 281

Einstein’s thought experiments on, 18-23

linear programming, 64

LISP (LISt Processor), 153-55, 163

pattern recognition modules compared with, 154, 155

Lloyd, Seth, 316n, 317n

Loebner, Hugh, 298n

Loebner Prize, 298n

logic, 38-39

logical positivism, 220

logic gates, 185

Lois, George, 113

love, 117-20

biochemical changes associated with, 118-19

evolutionary goals and, 119

pattern recognition modules and, 119-20

“Love Is the Drug,” 118

Lovelace, Ada Byron, Countess of, 190, 191

lucid dreaming, 72, 287n-88n

Lyell, Charles, 14-15, 114, 177

McCarthy, John, 153
McClelland, Shearwood, 225
McGinn, Colin, 200

magnetic data storage, growth in, 261, 301n-3n
magnetoencephalography, 129
Manchester Small-Scale Experimental Machine, 189
Mandelbrot set, 10-11 ,10

Marconi, Guglielmo, 253
Mark 1 Perceptron, 131-32, 134, 135, 189
Markov, Andrei Andreyevich, 143
Markram, Henry, 80-82, 124-27, 129
mass equivalent, of energy, 22-23
Mathematica, 171

“Mathematical Theory of Communication, A” (Shannon), 184

Mauchly, John, 189

Maudsley, Henry, 224

Maxwell, James Clerk, 20

Maxwell, Robert, 225

Mead, Carver, 194-95

medial geniculate nucleus, 97, 100

medicine, AI and, 6-7, 39, 108, 156, 160-61, 168

memes:

consciousness as, 211, 235
free will as, 235

memory, in computers, 185, 259, 260, 268, 301n-3n, 306n-7n

memory, memories, human:

abstract concepts in, 58-59

capacity of, 192-93

computers as extensions of, 169

consciousness vs., 28-29, 206-7, 217

dimming of, 29, 59

hippocampus and, 101-2

as ordered sequences of patterns, 27-29, 54

redundancy of, 59

unexpected recall of, 31-32, 54, 68-69
working, 101
Menabrea, Luigi, 190
metacognition, 200, 201
metaphors, 14-15, 113-17, 176-77
Michelson, Albert, 18, 19, 36, 114
Michelson-Morley experiment, 19, 36, 114
microtubules, 206, 207, 208, 274
Miescher, Friedrich, 16
mind, 11

pattern recognition theory of (PRTM), 5-6, 8, 11, 34-74, 79, 80, 86, 92,
111, 172, 217

thought experiments on, 199-247

mind-body problem, 221

Minsky, Marvin, 62, 133-35, 134, 199, 228

MIT Artificial Intelligence Laboratory, 134

MIT Picower Institute for Learning and Memory, 101

MobilEye, 159

modeling, complexity and, 37-38

Modha, Dharmendra, 128, 195, 271-72

momentum, 20-21

conservation of, 21-22

Money, John William, 118, 119

montane vole, 119

mood, regulation of, 106

Moore, Gordon, 251

Moore’s law, 251, 255, 268

moral intelligence, 201

moral systems, consciousness as basis of, 212-13

Moravec, Hans, 196

Morley, Edward, 18, 19, 36, 114

Moskovitz, Dustin, 156

motor cortex, 36, 99

motor nerves, 99

Mountcastle, Vernon, 36, 37, 94

Mozart, Leopold, 111

Mozart, Wolfgang Amadeus, 111, 112

MRI (magnetic resonance imaging), 129

spatial resolution of, 262-65, 263, 309n

MT (V5) visual cortex region, 83, 95

Muckli, Lars, 225

music, as universal to human culture, 62
mutations, simulated, 148

names, recalling, 32
National Institutes of Health, 129
natural selection, 76

geologic process as metaphor for, 14-15, 114, 177
see also evolution
Nature, 94

nematode nervous system, simulation of, 124

neocortex, 3, 7, 77, 78

AI reverse-engineering of, see neocortex, digital
bidirectional flow of information in, 85-86, 91
evolution of, 35-36

expansion of, through AI, 172, 266-72, 276

expansion of, through collaboration, 116

hierarchical order of, 41-53

learning process of, see learning

linear organization of, 250

as metaphor machine, 113

neural leakage in, 150-51

old brain as modulated by, 93-94, 105, 108

one-dimensional representations of multidimensional data in, 53, 66, 91,
141-42

pattern recognition in, see pattern recognition

pattern recognizers in, see pattern recognition modules

plasticity of, see brain plasticity

prediction by, 50-51, 52, 58, 60, 66-67, 250

PRTM as basic algorithm of, 6

pruning of unused connections in, 83, 90, 143, 174

redundancy in, 9, 224

regular grid structure of, 82-83, 84, 85, 129, 262
sensory input in, 58, 60

simultaneous processing of information in, 193

specific types of patterns associated with regions of, 86-87, 89-90, 91, 111,
152

structural simplicity of, 11

structural uniformity of, 36-37

structure of, 35-37, 38, 75-92

as survival mechanism, 79, 250

thalamus as gateway to, 100-101

total capacity of, 40, 280

total number of neurons in, 230

unconscious activity in, 228, 231, 233

unified model of, 24, 34-74

as unique to mammalian brain, 93, 286n

universal processing algorithm of, 86, 88, 90-91, 152, 272

see also cerebral cortex

neocortex, digital, 6-8, 41, 116-17, 121-78, 195

benefits of, 123-24, 247

bidirectional flow of information in, 173

as capable of being copied, 247

critical thinking module for, 176, 197

as extension of human brain, 172, 276

HHMMs in, 174-75

hierarchical structure of, 173

knowledge bases of, 177

learning in, 127-28, 175-76

metaphor search module in, 176-77

moral education of, 177-78

pattern redundancy in, 175

simultaneous searching in, 177

structure of, 172-78

virtual neural connections in, 173-74

neocortical columns, 36-37, 38, 90, 124-25

nervous systems, 2

neural circuits, unreliability of, 185

neural implants, 243, 245

neural nets, 131-35, 144, 155

algorithm for, 291n-97n

feedforward, 134, 135

learning in, 132-33

neural processing:

digital emulation of, 195-97

massive parallelism of, 192, 193, 195

speed of, 192, 195

neuromorphic chips, 194-95, 196

neuromuscular junction, 99

neurons, 2, 36, 38, 43, 80, 172

neurotransmitters, 105-7

new brain, see neocortex

Newell, Allen, 181

New Kind of Science, A (Wolfram), 236, 239
Newton, Isaac, 94
Nietzsche, Friedrich, 117

nonbiological systems, as capable of being copied, 247
nondestructive imaging techniques, 127, 129, 264, 312n-13n
nonmammals, reasoning by, 286n

noradrenaline, 107
norepinephrine, 118

Notes from Underground (Dostoevsky), 199

Nuance Speech Technologies, 6-7, 108, 122, 152, 161, 162, 168

nucleus accumbens, 77, 105

Numenta, 156

NuPIC, 156

obsessive-compulsive disorder, 118

occipital lobe, 36

old brain, 63, 71, 90, 93-108

neocortex as modulator of, 93-94, 105, 108

sensory pathway in, 94-98

olfactory system, 100

Oluseun, Oluseyi, 204

OmniPage, 122

One Hundred Years of Solitude (Garcia Marquez), 283n-85n

On Intelligence (Hawkins and Blakeslee), 73, 156

On the Origin of Species (Darwin), 15-16

optical character recognition (OCR), 122

optic nerve, 95, 100

channels of, 94-95, 96

organisms, simulated, evolution of, 147-53

overfitting problem, 150

oxytocin, 119

pancreas, 37

panprotopsychism, 203, 213
Papert, Seymour, 134-35 ,134
parameters, in pattern recognition:

“God,” 147

importance, 42, 48-49, 60, 66, 67

size, 42, 49-50, 60, 61, 66, 67, 73-74, 91-92, 173

size variability, 42, 49-50, 67, 73-74, 91-92

Parker, Sean, 156

Parkinson’s disease, 243, 245

particle physics, see quantum mechanics

Pascal, Blaise, 117

patch-clamp robotics, 125-26 ,126

pattern recognition, 195

of abstract concepts, 58-59

as based on experience, 50, 90, 273-74

as basic unit of learning, 80-81

bidirectional flow of information in, 52, 58, 68

distortions and, 30

eye movement and, 73

as hierarchical, 33, 90, 138, 142

of images, 48

invariance and, see invariance, in pattern recognition

learning as simultaneous with, 63

list combining in, 60-61

in neocortex, see pattern recognition modules

redundancy in, 39-40, 57, 60, 64, 185

pattern recognition modules, 35-41, 42, 90, 198

autoassociation in, 60-61

axons of, 42, 43, 66, 67, 113, 173

bidirectional flow of information to and from thalamus, 100-101
dendrites of, 42, 43, 66, 67
digital, 172-73, 175, 195

expectation (excitatory) signals in, 42, 52, 54, 60, 67, 73, 85, 91, 100, 112,
173, 175, 196-97

genetically determined structure of, 80

“God parameter” in, 147

importance parameters in, 42, 48-49, 60, 66, 67

inhibitory signals in, 42, 52-53, 67, 85, 91, 100, 173

input in, 41-42, 42, 53-59

love and, 119-20

neural connections between, 90

as neuronal assemblies, 80-81

one-dimensional representation of multidimensional data in, 53, 66, 91,
141-42

prediction by, 50-51, 52, 58, 60, 66-67
redundancy of, 42, 43, 48, 91
sequential processing of information by, 266
simultaneous firings of, 57-58, 57, 146

size parameters in, 42, 49-50, 60, 61, 66, 67, 73-74, 91-92, 173
size variability parameters in, 42, 67, 73-74, 91-92, 173
of sounds, 48

thresholds of, 48, 52-53, 60, 66, 67, 111-12, 173
total number of, 38, 40, 41, 113, 123, 280
universal algorithm of, 111, 275

pattern recognition theory of mind (PRTM), 5-6, 8, 11, 34-74, 79, 80, 86,
92, 111, 172, 217
patterns:

hierarchical ordering of, 41-53

higher-level patterns attached to, 43, 45, 66, 67

input in, 41, 42, 44, 66, 67

learning of, 63-64, 90

name of, 42-43

output of, 42, 44, 66, 67

redundancy and, 64

specific areas of neocortex associated with, 86-87, 89-90, 91, 111, 152

storing of, 64-65

structure of, 41-53

Patterns, Inc., 156

Pavlov, Ivan Petrovich, 216

Penrose, Roger, 207-8, 274

perceptions, as influenced by expectations and interpretations, 31
perceptrons, 131-35

Perceptrons (Minsky and Papert), 134-35 ,134
phenylethylamine, 118

Philosophical Investigations (Wittgenstein), 221
phonemes, 61, 135, 137, 146, 152
photons, 20-21
physics, 37

computational capacity and, 281, 316n-19n

laws of, 37, 267

standard model of, 2

see also quantum mechanics

Pinker, Steven, 76-77, 278

pituitary gland, 77

Plato, 212, 221, 231

pleasure, in old and new brains, 104-8

Poggio, Tomaso, 85, 159

posterior ventromedial nucleus (VMpo), 99-100, 99
prairie vole, 119

predictable outcomes, determined outcomes vs., 26, 239

President’s Council of Advisors on Science and Technology, 269
price/performance, of computation, 4-5, 250-51, 257, 257, 267-68, 301n-
3 n

Principia Mathematica (Russell and Whitehead), 181
probability fields, 218-19, 235-36
professional knowledge, 39-40
proteins, reverse-engineering of, 4-5

qualia, 203-5, 210, 211
quality of life, perception of, 277-78
quantum computing, 207-9, 274
quantum mechanics, 218-19
observation in, 218-19, 235-36
randomness vs. determinism in, 236
Quinlan, Karen Ann, 101

Ramachandran, Vilayanur Subramanian “Rama,” 230

random access memory:

growth in, 259, 260, 301n-3n, 306n-7n

three-dimensional, 268

randomness, determinism and, 236

rationalization, see confabulation

reality, hierarchical nature of, 4, 56, 90, 94, 172

recursion, 3, 7-8, 56, 65, 91, 153, 156, 177, 188

“Red” (Oluseum), 204

redundancy, 9, 39-40, 64, 184, 185, 197, 224
in genome, 271, 314n, 315n
of memories, 59

of pattern recognition modules, 42, 43, 48, 91
thinking and, 57
religious ecstacy, 118

“Report to the President and Congress, Designing a Digital Future”
(President’s Council of Advisors on Science and Technology), 269
retina, 95

reverse-engineering:
of biological systems, 4-5

of human brain, see brain, human, computer emulation of; neocortex,
digital

Rosenblatt, Frank, 131, 133, 134, 135, 191

Roska, Boton, 94
Rothblatt, Martine, 278

routine tasks, as series of hierarchical steps, 32-33

Rowling, J. K., 117, 121

Roxy Music, 118

Russell, Bertrand, 104, 181, 220

Rutter, Brad, 165

saccades, 73
Salk Institute, 89
same-sex marriage, 278
Sandberg, Anders, 129-30, 318n
Schopenhauer, Arthur, 235, 240
science:

as based on objective measurement, 211
specialization in, 115
Science, 82-83

“scientist’s pessimism,” 272-73

Searle, John, 170, 201, 205, 206, 222

“Chinese room” thought experiment of, 170, 274-75

Seinfeld (TV show), 75

selective serotonin reuptake inhibitors, 106

self-organizing systems, 144, 147, 149, 150, 154-55, 162, 168, 171-72,
175, 197, 270
sensorimotor area, 77
sensory cortex, 99
sensory nerves, 99
sensory organs, 58
sensory receptors, 99

sensory-touch pathway, 58, 60, 94-98, 95, 97-100, 97, 99

serotonin, 105, 106, 107, 118

Seung, Sebastian, 10

Sex and the City (TV show), 117

sexual reproduction, 118

simulated, 148

Shakespeare, William, 39, 114-15, 209
Shannon, Claude, 183-84, 190
Shashua, Amnon, 159
Shaw, J. C., 181

Short Circuit (film), 210
Simon, Herbert A., 37-38, 181
singularity, 194

Singularity Is Near, The (Kurzweil), 4, 5, 196, 251, 253, 256, 257, 267,
268-69, 271, 274, 316n-19n

“Singularity Isn’t Near, The” (Allen and Greaves), 266-72

Siri, 7, 72, 116, 123, 153, 161-62, 164, 168, 171

size parameters, 42, 60, 66, 67, 73-74, 91-92

size variability parameters, 42, 49-50, 67, 73-74, 91-92

Skinner, B. F., 13

Smullyan, Raymond, 241

Society of Mind, The (Minsky), 62, 199

Sonnet 73 (Shakespeare), 114-15

SOPA (Stop Online Piracy Act), 279

sparse coding, 95-96, 135-41

specialization, increasing, 115

spectrograms, 135, 136,137

speech recognition software, 49-50, 51, 53, 61, 72-74, 92, 115-16, 122-23,
128, 135-46, 145, 198, 273
GAs in, 149-50, 152
HHMM in, 149-50, 152-53
speed of light, 281

Einstein’s thought experiments on, 18-23

Sperry, Roger W., 218

spinal cord, 36, 99

spindle neurons, 109-11

split-brain patients, 70, 226-27

Stanford Encyclopedia of Philosophy, The, 232

Star Trek: The Next Generation (TV show), 210

Star Wars films, 210

stochastic variation, 9

supercomputer power, growth in, 258, 301n-3n
survival:

as evolutionary goal, 79, 104, 242
as individual goal, 242
Sutherland, Stuart, 211
SyNAPSE chips, 195, 196
Szent-Gyorgyi, Albert, 93

taboos, dreams and, 71-72
Taylor, J. G., 75

technology, as compensating for human limitation, 3, 27, 276, 279

Technology Review, 266

Tegmark, Max, 208

Terminator films, 210

testosterone, 118

thalamus, 36, 77, 95, 97, 97, 98-101

as gateway to neocortex, 100-101

thermodynamics, 177

laws of, 37, 267

Thiel, Peter, 156

thinking:

computing compared with, 26-27
disorderliness of, 55, 69
language as translation of, 56, 68
limitations to, 23-24, 27
redundancy and, 57
as statistical analysis, 170
statistical probability and, 270-71
thought experiments on, 24, 25-33
undirected vs. directed, 54-55, 68-69
see also hierarchical thinking
thought experiments, 114
“Chinese room,” 170, 274-75
on computer consciousness, 202, 210
of Darwin, 14-16, 23
of Einstein, 18-23, 114, 117
on identity, 242-47
on the mind, 199-247
on thinking, 24, 25-33
of Turing, 185-87, 188
Thrun, Sebastian, 158

time, Einstein’s thought experiments on, 19-20
tool making, by humans, 3, 27, 276, 279
Tractatus Logico-Philosophicus (Wittgenstein), 219-21
Transcend: Nine Steps to Living Well Forever (Kurzweil and Grossman),
287n-88n

Transformers films, 210

transistors:

per chip, growth in, 258, 301n-3n

price decrease in, 260, 304n-6n

three-dimensional, 268

Turing, Alan, 121, 159-60, 185, 191

thought experiments of, 185-87, 188

unsolvable problem theorem of, 187, 207-8

Turing machine, 185-87, 186, 188, 192, 207-8

Turing test, 159-60, 169, 170, 178, 191, 213, 214, 233, 276, 298n

UIMA (Unstructured Information Management Architecture), 167-68
Ulam, Stan, 194
Unitarianism, 222

universality of computation, 26, 181-82, 185, 188, 192, 207
universe, as capable of encoding information, 2
University College (London), 118
unsolvable problems, Turing’s theorem of, 187, 207-8

vasopressin, 119

vector quantization, 135, 138-39, 145

invariance and, 141

ventral pallidum, 105

Vicarious Systems, 156

visual association, 77

visual cortex, 7, 77, 83, 95, 193

of congenitally blind people, 87

digital simulation of, 128

hierarchical structure of, 85-86

VI region, 83, 85, 87, 95, 100

V2 region of, 83, 85, 87, 95

V5 (MT) region of, 83, 95

visual information processing, 94-96, 95, 96

visual pathway, 95

visual recognition systems, 53

von Neumann, John, 179, 186-89, 190, 195

brain/computer comparison of, 191-95

stored program concept of, 186-87, 188

von Neumann machine, 187-89, 190, 193

Voyage of the Beagle (Darwin), 14

Wall-E (film), 210

Watson (IBM computer), 6-7, 108, 157-58, 159, 160, 165, 166, 167-68,
171, 172, 178, 200, 232-33, 239, 247, 265, 270-71, 274
Watson, James D., 8-9, 16-17
Watts, Lloyd, 96

wave function, collapse of, 218-19, 235-36
Wedeen, Van J., 82-83, 90, 129, 262
Werblin, Frank S., 94-95
Whitehead, Alfred North, 181

Whole Brain Emulation: A Roadmap (Sandberg and Bostrom), 129-30,
130,131

Wiener, Norbert, 115, 143

Wikipedia, 6, 156, 166, 170, 176, 232, 270, 279

Wittgenstein, Ludwig, 219-21

Wolfram, Stephen, 170-71, 177, 236-39

Wolfram Alpha, 161, 170-72, 177

Wolfram Research, 170-71

working memory, 101

World War I, 278

World War II, 187, 278

writing, as backup system, 123-24

Young, Thomas, 18

Z-3 computer, 189
Zuo, Yi, 89
Zuse, Konrad, 189

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